Systems and methods for detecting an object

ABSTRACT

Systems and methods are provided for detecting an object in front of a vehicle. In one implementation, an object detecting system includes an image capture device configured to acquire a plurality of images of an area, a data interface, and a processing device programmed to compare a first image to a second image to determine displacement vectors between pixels. The processing device is also programmed to search for a region of coherent expansion that is a set of pixels in at least one of the first image and the second image, for which there exists a common focus of expansion and a common scale magnitude such that the set of pixels satisfy a relationship between pixel positions, displacement vectors, the common focus of expansion, and the common scale magnitude. The processing device is further programmed to identify presence of a substantially upright object based on the set of pixels.

CROSS REFERENCE TO RELATED APPLICATION

This application is a national phase application of InternationalApplication No. PCT/IB2015/001593, filed on Jun. 3, 2015, which claimsthe benefit of priority to U.S. Provisional Patent Application No.62/006,912, filed on Jun. 3, 2014. The contents of all of theabove-mentioned applications are incorporated herein by reference intheir entirety.

BACKGROUND

I. Technical Field

The present disclosure relates generally to autonomous vehiclenavigation and, more specifically, to systems and methods that usecameras to detect an object in front of the vehicle.

II. Background Information

As technology continues to advance, the goal of a fully autonomousvehicle that is capable of navigating on roadways is on the horizon.Primarily, an autonomous vehicle may be able to identify its environmentand navigate without input from a human operator. Autonomous vehiclesmay also take into account a variety of factors and make appropriatedecisions based on those factors to safely and accurately reach anintended destination. For example, various objects such as othervehicles and pedestrians—are encountered when a vehicle typicallytravels a roadway. Autonomous driving systems may recognize theseobjects in a vehicle's environment and take appropriate and timelyaction to avoid collisions. Additionally, autonomous driving systems mayidentify other indicators—such as traffic signals, traffic signs, andlane markings—that regulate vehicle movement (e.g., when the vehiclemust stop and may go, a speed at which the vehicle must not exceed,where the vehicle must be positioned on the roadway, etc.). Autonomousdriving systems may need to determine when a vehicle should changelanes, turn at intersections, change roadways, etc. As is evident fromthese examples, many factors may need to be addressed in order toprovide an autonomous vehicle that is capable of navigating safely andaccurately.

SUMMARY

Embodiments consistent with the present disclosure provide systems andmethods for autonomous vehicle navigation. The disclosed embodiments mayuse cameras to provide autonomous vehicle navigation features. Forexample, consistent with the disclosed embodiments, the disclosedsystems may include one, two, or more cameras that monitor theenvironment of a vehicle and cause a navigational response based on ananalysis of images captured by one or more of the cameras.

Consistent with a disclosed embodiment, an object detecting system for avehicle is provided. The system may include at least one image capturedevice configured to acquire a plurality of images of an area in frontof the vehicle. The system may include a data interface, and at leastone processing device programmed to receive the plurality of images viathe data interface. The at least one processing device may also beprogrammed to compare a first image to a second image to determinedisplacement vectors between pixels of the first image and pixels of thesecond image. The at least one processing device may further beprogrammed to search for a region of coherent expansion that is a set ofpixels in at least one of the first image and the second image, forwhich there exists a common focus of expansion and a common scalemagnitude such that the set of pixels satisfy a relationship betweenpixel positions, displacement vectors, the common focus of expansion,and the common scale magnitude. The at least one processing device mayfurther be programmed to identify presence of a substantially uprightobject in at least one of the first image and the second image based onthe set of pixels.

Consistent with another disclosed embodiment, a vehicle is provided. Thevehicle may include a body. The vehicle may include at least one imagecapture device configured to acquire a plurality of images of an area infront of the vehicle. The system may include a data interface and atleast one processing device programmed to receive the plurality ofimages via the data interface. The at least one processing device mayalso be programmed to compare a first image to a second image todetermine displacement vectors between pixels of the first image andpixels of the second image. The at least one processing device mayfurther be programmed to search for a region of coherent expansion thatis a set of pixels in at least one of the first image and the secondimage, for which there exists a common focus of expansion and a commonscale magnitude such that the set of pixels satisfy a relationshipbetween pixel positions, displacement vectors, the common focus ofexpansion, and the common scale magnitude. The at least one processingdevice may further be programmed to identify presence of a substantiallyupright object in at least one of the first image and the second imagebased on the set of pixels.

Consistent with yet another disclosed embodiment, a method for detectingan object in front of a vehicle is provided. The method may includeacquiring, via at least one image capture device, a plurality of imagesof an area in front of the vehicle. The method may also includereceiving, via a processing device, the plurality of images. The methodmay also include comparing, via the processing device, a first image toa second image to determine displacement vectors between pixels of thefirst image and pixels of the second image. The method may furtherinclude searching for, via the processing device, a region of coherentexpansion that is a set of pixels in at least one of the first image andthe second image, for which there exists a common focus of expansion anda common scale magnitude such that the set of pixels satisfy arelationship between pixel positions, displacement vectors, the commonfocus of expansion, and the common scale magnitude. The method mayfurther include identifying, via the processing device, presence of asubstantially upright object in at least one of the first image and thesecond image based on the set of pixels.

Consistent with another disclosed embodiment, an object detecting systemfor a vehicle is provided. The object detecting system may include atleast one image capture device configured to acquire a plurality ofimages of an area in front of the vehicle. The system may also include adata interface and at least one processing device programmed to receivethe plurality of images via the data interface. The at least oneprocessing device may also be programmed to compare a first image to asecond image, and based on the comparison, determine a focus ofexpansion of a crossing object moving in a direction that crosses atravel direction of the vehicle. The at least one processing device mayalso be programmed to determine whether a horizontal coordinate of thefocus of expansion stays around a center in the horizontal dimension ofthe crossing object during a period of time. The at least one processingdevice may further be programmed to cause the vehicle to brake based onthe determination that the horizontal coordinate of the focus ofexpansion stays around the center in the horizontal dimension of thecrossing object during the period of time.

Consistent with another disclosed embodiment, a vehicle is provided. Thevehicle may include a body, and at least one image capture deviceconfigured to acquire a plurality of images of an area in front of thevehicle. The system may also include a data interface and at least oneprocessing device programmed to receive the plurality of images via thedata interface. The at least one processing device may also beprogrammed to compare a first image to a second image, and based on thecomparison, determine a focus of expansion of a crossing object movingin a direction that crosses a travel direction of the vehicle. The atleast one processing device may also be programmed to determine whethera horizontal coordinate of the focus of expansion stays around a centerin the horizontal dimension of the crossing object during a period oftime. The at least one processing device may further be programmed tocause the vehicle to brake based on the determination that thehorizontal coordinate of the focus of expansion stays around the centerin the horizontal dimension of the crossing object during the period oftime.

Consistent with another disclosed embodiment, a method for detecting anobject in front of a vehicle is provided. The method may includeacquiring, via at least one image capture device, a plurality of imagesof an area in front of the vehicle. The method may include receiving,via a processing device, the plurality of images. The method may alsoinclude comparing, via the processing device, a first image to a secondimage, and based on the comparison, determining a focus of expansion ofa crossing object moving in a direction that crosses a travel directionof the vehicle. The method may also include determining, via theprocessing device, whether a horizontal coordinate of the focus ofexpansion stays around a center in the horizontal dimension of thecrossing object during a period of time. The method may also includecausing, via the processing device, the vehicle to brake based on thedetermination that the horizontal coordinate of the focus of expansionstays around a center in the horizontal dimension of the crossing objectduring the period of time.

Consistent with other disclosed embodiments, non-transitorycomputer-readable storage media may store program instructions, whichare executed by at least one processing device and perform any of themethods described herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various disclosed embodiments. Inthe drawings:

FIG. 1 is a diagrammatic representation of an exemplary systemconsistent with the disclosed embodiments.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle including a system consistent with the disclosed embodiments.

FIG. 2B is a diagrammatic top view representation of the vehicle andsystem shown in FIG. 2A consistent with the disclosed embodiments.

FIG. 2C is a diagrammatic top view representation of another embodimentof a vehicle including a system consistent with the disclosedembodiments.

FIG. 2D is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2E is a diagrammatic top view representation of yet anotherembodiment of a vehicle including a system consistent with the disclosedembodiments.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems consistent with the disclosed embodiments.

FIG. 3A is a diagrammatic representation of an interior of a vehicleincluding a rearview mirror and a user interface for a vehicle imagingsystem consistent with the disclosed embodiments.

FIG. 3B is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 3C is an illustration of the camera mount shown in FIG. 3B from adifferent perspective consistent with the disclosed embodiments.

FIG. 3D is an illustration of an example of a camera mount that isconfigured to be positioned behind a rearview mirror and against avehicle windshield consistent with the disclosed embodiments.

FIG. 4 is an exemplary block diagram of a memory configured to storeinstructions for performing one or more operations consistent with thedisclosed embodiments.

FIG. 5A is a flowchart showing an exemplary process for causing one ormore navigational responses based on monocular image analysis consistentwith disclosed embodiments.

FIG. 5B is a flowchart showing an exemplary process for detecting one ormore vehicles and/or pedestrians in a set of images consistent with thedisclosed embodiments.

FIG. 5C is a flowchart showing an exemplary process for detecting roadmarks and/or lane geometry information in a set of images consistentwith the disclosed embodiments.

FIG. 5D is a flowchart showing an exemplary process for detectingtraffic lights in a set of images consistent with the disclosedembodiments.

FIG. 5E is a flowchart showing an exemplary process for causing one ormore navigational responses based on a vehicle path consistent with thedisclosed embodiments.

FIG. 5F is a flowchart showing an exemplary process for determiningwhether a leading vehicle is changing lanes consistent with thedisclosed embodiments.

FIG. 6 is a flowchart showing an exemplary process for causing one ormore navigational responses based on stereo image analysis consistentwith the disclosed embodiments.

FIG. 7 is a flowchart showing an exemplary process for causing one ormore navigational responses based on an analysis of three sets of imagesconsistent with the disclosed embodiments.

FIG. 8 is a diagrammatic representation of an exemplary vehicleincluding an object detecting system traveling on a road behind anothervehicle consistent with the disclosed embodiments.

FIG. 9 is an exemplary block diagram of a memory that may storeinstructions for performing one or more operations for detecting anobject in front of the vehicle consistent with the disclosedembodiments.

FIG. 10A is an exemplary image of an area showing vectors associatedwith pixels consistent with the disclosed embodiments.

FIG. 10B is a schematic illustration of an exemplary relationshipbetween the focus of expansion and the scale magnitude consistent withthe disclosed embodiments.

FIG. 10C shows an exemplary method of identifying a substantiallyupright object based on selected pixels consistent with the disclosedembodiments.

FIG. 11 is a flowchart showing an exemplary process for detectingpresence of an object in front of the vehicle consistent with thedisclosed embodiments.

FIG. 12A is an image of a static environment consistent with thedisclosed embodiments.

FIG. 12B is a three-dimensional cube space illustrating an exemplaryrelationship between the focus of expansion and the scale magnitudecorresponding to the static environment of FIG. 12A consistent with thedisclosed embodiments.

FIG. 13A is an image of a environment having another vehicle static inthe environment or traveling in a direction parallel to the travelingdirection of the vehicle where object detecting system is installedconsistent with the disclosed embodiments.

FIG. 13B is a three-dimensional cube space illustrating an exemplaryrelationship between the focus of expansion and the scale magnitudecorresponding to the image of FIG. 13A consistent with the disclosedembodiments.

FIG. 13C is an image of a environment having another vehicle travelinglongitudinally in front of and in alignment with the vehicle whereobject detecting system is installed consistent with the disclosedembodiments.

FIG. 13D is a three-dimensional cube space illustrating an exemplaryrelationship between the focus of expansion and the scale magnitudecorresponding to the image of FIG. 13C consistent with the disclosedembodiments.

FIG. 14A is an image of an environment superimposed with an exemplaryoptical flow field consistent with the disclosed embodiments.

FIG. 14B is a three-dimensional cube space illustrating two exemplaryfocuses of expansion corresponding to the vehicle object and thepedestrian object shown in FIG. 14A consistent with the disclosedembodiments.

FIG. 15A shows a diagram illustrating an exemplary method of detecting acrossing-to-impact object consistent with the disclosed embodiment.

FIG. 15B shows an image having a pedestrian crossing the road in frontof the vehicle consistent with the disclosed embodiment.

FIG. 15C shows an image having a pedestrian crossing the road in frontof the vehicle acquired at a time after the image shown in FIG. 15B isacquired, consistent with the disclosed embodiments.

FIG. 16 is a flowchart showing an exemplary process for detecting anobject in front of the vehicle consistent with the disclosedembodiments.

FIG. 17 illustrates an exemplary method of detecting an objectconsistent with the disclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

FIG. 1 is a block diagram representation of a system 100 consistent withthe exemplary disclosed embodiments. System 100 may include variouscomponents depending on the requirements of a particular implementation.In some embodiments, system 100 may include a processing unit 110, animage acquisition unit 120, a position sensor 130, one or more memoryunits 140, 150, a map database 160, and a user interface 170. Processingunit 110 may include one or more processing devices. In someembodiments, processing unit 110 may include an applications processor180, an image processor 190, or any other suitable processing device.Similarly, image acquisition unit 120 may include any number of imageacquisition devices and components depending on the requirements of aparticular application. In some embodiments, image acquisition unit 120may include one or more image capture devices (e.g., cameras), such asimage capture device 122, image capture device 124, and image capturedevice 126. System 100 may also include a data interface 128communicatively connecting processing device 110 to image acquisitiondevice 120. For example, data interface 128 may include any wired and/orwireless link or links for transmitting image data acquired by imageaccusation device 120 to processing unit 110.

Both applications processor 180 and image processor 190 may includevarious types of processing devices. For example, either or both ofapplications processor 180 and image processor 190 may include amicroprocessor, preprocessors (such as an image preprocessor), graphicsprocessors, a central processing unit (CPU), support circuits, digitalsignal processors, integrated circuits, memory, or any other types ofdevices suitable for running applications and for image processing andanalysis. In some embodiments, applications processor 180 and/or imageprocessor 190 may include any type of single or multi-core processor,mobile device microcontroller, central processing unit, etc. Variousprocessing devices may be used, including, for example, processorsavailable from manufacturers such as Intel®, AMD®, etc. and may includevarious architectures (e.g., x86 processor, ARM®, etc.).

In some embodiments, applications processor 180 and/or image processor190 may include any of the EyeQ series of processor chips available fromMobileye®. These processor designs each include multiple processingunits with local memory and instruction sets. Such processors mayinclude video inputs for receiving image data from multiple imagesensors and may also include video out capabilities. In one example, theEyeQ2® uses 90 nm-micron technology operating at 332 Mhz. The EyeQ2®architecture consists of two floating point, hyper-thread 32-bit RISCCPUs (MIPS32® 34K® cores), five Vision Computing Engines (VCE), threeVector Microcode Processors (VMP®), Denali 64-bit Mobile DDR Controller,128-bit internal Sonics Interconnect, dual 16-bit Video input and 18-bitVideo output controllers, 16 channels DMA and several peripherals. TheMIPS34K CPU manages the five VCEs, three VMP™ and the DMA, the secondMIPS34K CPU and the multi-channel DMA as well as the other peripherals.The five VCEs, three VMP® and the MIPS34K CPU can perform intensivevision computations required by multi-function bundle applications. Inanother example, the EyeQ3®, which is a third generation processor andis six times more powerful that the EyeQ2®, may be used in the disclosedembodiments.

Any of the processing devices disclosed herein may be configured toperform certain functions. Configuring a processing device, such as anyof the described EyeQ processors or other controller or microprocessor,to perform certain functions may include programming of computerexecutable instructions and making those instructions available to theprocessing device for execution during operation of the processingdevice. In some embodiments, configuring a processing device may includeprogramming the processing device directly with architecturalinstructions. In other embodiments, configuring a processing device mayinclude storing executable instructions on a memory that is accessibleto the processing device during operation. For example, the processingdevice may access the memory to obtain and execute the storedinstructions during operation.

While FIG. 1 depicts two separate processing devices included inprocessing unit 110, more or fewer processing devices may be used. Forexample, in some embodiments, a single processing device may be used toaccomplish the tasks of applications processor 180 and image processor190. In other embodiments, these tasks may be performed by more than twoprocessing devices.

Processing unit 110 may comprise various types of devices. For example,processing unit 110 may include various devices, such as a controller,an image preprocessor, a central processing unit (CPU), supportcircuits, digital signal processors, integrated circuits, memory, or anyother types of devices for image processing and analysis. The imagepreprocessor may include a video processor for capturing, digitizing andprocessing the imagery from the image sensors. The CPU may comprise anynumber of microcontrollers or microprocessors. The support circuits maybe any number of circuits generally well known in the art, includingcache, power supply, clock and input-output circuits. The memory maystore software that, when executed by the processor, controls theoperation of the system. The memory may include databases and imageprocessing software. The memory may comprise any number of random accessmemories, read only memories, flash memories, disk drives, opticalstorage, tape storage, removable storage and other types of storage. Inone instance, the memory may be separate from the processing unit 110.In another instance, the memory may be integrated into the processingunit 110.

Each memory 140, 150 may include software instructions that whenexecuted by a processor (e.g., applications processor 180 and/or imageprocessor 190), may control operation of various aspects of system 100.These memory units may include various databases and image processingsoftware. The memory units may include random access memory, read onlymemory, flash memory, disk drives, optical storage, tape storage,removable storage and/or any other types of storage. In someembodiments, memory units 140, 150 may be separate from the applicationsprocessor 180 and/or image processor 190. In other embodiments, thesememory units may be integrated into applications processor 180 and/orimage processor 190.

Position sensor 130 may include any type of device suitable fordetermining a location associated with at least one component of system100. In some embodiments, position sensor 130 may include a GPSreceiver. Such receivers can determine a user position and velocity byprocessing signals broadcasted by global positioning system satellites.Position information from position sensor 130 may be made available toapplications processor 180 and/or image processor 190.

User interface 170 may include any device suitable for providinginformation to or for receiving inputs from one or more users of system100. In some embodiments, user interface 170 may include user inputdevices, including, for example, a touchscreen, microphone, keyboard,pointer devices, track wheels, cameras, knobs, buttons, etc. With suchinput devices, a user may be able to provide information inputs orcommands to system 100 by typing instructions or information, providingvoice commands, selecting menu options on a screen using buttons,pointers, or eye-tracking capabilities, or through any other suitabletechniques for communicating information to system 100.

User interface 170 may be equipped with one or more processing devicesconfigured to provide and receive information to or from a user andprocess that information for use by, for example, applications processor180. In some embodiments, such processing devices may executeinstructions for recognizing and tracking eye movements, receiving andinterpreting voice commands, recognizing and interpreting touches and/orgestures made on a touchscreen, responding to keyboard entries or menuselections, etc. In some embodiments, user interface 170 may include adisplay, speaker, tactile device, and/or any other devices for providingoutput information to a user.

Map database 160 may include any type of database for storing map datauseful to system 100. In some embodiments, map database 160 may includedata relating to the position, in a reference coordinate system, ofvarious items, including roads, water features, geographic features,businesses, points of interest, restaurants, gas stations, etc. Mapdatabase 160 may store not only the locations of such items, but alsodescriptors relating to those items, including, for example, namesassociated with any of the stored features. In some embodiments, mapdatabase 160 may be physically located with other components of system100. Alternatively or additionally, map database 160 or a portionthereof may be located remotely with respect to other components ofsystem 100 (e.g., processing unit 110). In such embodiments, informationfrom map database 160 may be downloaded over a wired or wireless dataconnection to a network (e.g., over a cellular network and/or theInternet, etc.).

Image capture devices 122, 124, and 126 may each include any type ofdevice suitable for capturing at least one image from an environment.Moreover, any number of image capture devices may be used to acquireimages for input to the image processor. Some embodiments may includeonly a single image capture device, while other embodiments may includetwo, three, or even four or more image capture devices. Image capturedevices 122, 124, and 126 will be further described with reference toFIGS. 2B-2E, below.

System 100, or various components thereof, may be incorporated intovarious different platforms. In some embodiments, system 100 may beincluded on a vehicle 200, as shown in FIG. 2A. For example, vehicle 200may be equipped with a processing unit 110 and any of the othercomponents of system 100, as described above relative to FIG. 1. Whilein some embodiments vehicle 200 may be equipped with only a single imagecapture device (e.g., camera), in other embodiments, such as thosediscussed in connection with FIGS. 2B-2E, multiple image capture devicesmay be used. For example, either of image capture devices 122 and 124 ofvehicle 200, as shown in FIG. 2A, may be part of an ADAS (AdvancedDriver Assistance Systems) imaging set.

The image capture devices included on vehicle 200 as part of the imageacquisition unit 120 may be positioned at any suitable location. In someembodiments, as shown in FIGS. 2A-2E and 3A-3C, image capture device 122may be located in the vicinity of the rearview mirror. This position mayprovide a line of sight similar to that of the driver of vehicle 200,which may aid in determining what is and is not visible to the driver.Image capture device 122 may be positioned at any location near therearview mirror, but placing image capture device 122 on the driver sideof the mirror may further aid in obtaining images representative of thedriver's field of view and/or line of sight.

Other locations for the image capture devices of image acquisition unit120 may also be used. For example, image capture device 124 may belocated on or in a bumper of vehicle 200. Such a location may beespecially suitable for image capture devices having a wide field ofview. The line of sight of bumper-located image capture devices can bedifferent from that of the driver and, therefore, the bumper imagecapture device and driver may not always see the same objects. The imagecapture devices (e.g., image capture devices 122, 124, and 126) may alsobe located in other locations. For example, the image capture devicesmay be located on or in one or both of the side mirrors of vehicle 200,on the roof of vehicle 200, on the hood of vehicle 200, on the trunk ofvehicle 200, on the sides of vehicle 200, mounted on, positioned behind,or positioned in front of any of the windows of vehicle 200, and mountedin or near light figures on the front and/or back of vehicle 200, etc.

In addition to image capture devices, vehicle 200 may include variousother components of system 100. For example, processing unit 110 may beincluded on vehicle 200 either integrated with or separate from anengine control unit (ECU) of the vehicle. Vehicle 200 may also beequipped with a position sensor 130, such as a GPS receiver and may alsoinclude a map database 160 and memory units 140 and 150.

FIG. 2A is a diagrammatic side view representation of an exemplaryvehicle imaging system consistent with the disclosed embodiments. FIG.2B is a diagrammatic top view illustration of the embodiment shown inFIG. 2A. As illustrated in FIG. 2B, the disclosed embodiments mayinclude a vehicle 200 including in its body a system 100 with a firstimage capture device 122 positioned in the vicinity of the rearviewmirror and/or near the driver of vehicle 200, a second image capturedevice 124 positioned on or in a bumper region (e.g., one of bumperregions 210) of vehicle 200, and a processing unit 110.

As illustrated in FIG. 2C, image capture devices 122 and 124 may both bepositioned in the vicinity of the rearview mirror and/or near the driverof vehicle 200. Additionally, while two image capture devices 122 and124 are shown in FIGS. 2B and 2C, it should be understood that otherembodiments may include more than two image capture devices. Forexample, in the embodiments shown in FIGS. 2D and 2E, first, second, andthird image capture devices 122, 124, and 126, are included in thesystem 100 of vehicle 200.

As illustrated in FIG. 2D, image capture device 122 may be positioned inthe vicinity of the rearview mirror and/or near the driver of vehicle200, and image capture devices 124 and 126 may be positioned on or in abumper region (e.g., one of bumper regions 210) of vehicle 200. And asshown in FIG. 2E, image capture devices 122, 124, and 126 may bepositioned in the vicinity of the rearview mirror and/or near the driverseat of vehicle 200. The disclosed embodiments are not limited to anyparticular number and configuration of the image capture devices, andthe image capture devices may be positioned in any appropriate locationwithin and/or on vehicle 200.

It is to be understood that the disclosed embodiments are not limited tovehicles and could be applied in other contexts. It is also to beunderstood that disclosed embodiments are not limited to a particulartype of vehicle 200 and may be applicable to all types of vehiclesincluding automobiles, trucks, trailers, and other types of vehicles.

The first image capture device 122 may include any suitable type ofimage capture device. Image capture device 122 may include an opticalaxis. In one instance, the image capture device 122 may include anAptina M9V024 WVGA sensor with a global shutter. In other embodiments,image capture device 122 may provide a resolution of 1280×960 pixels andmay include a rolling shutter. Image capture device 122 may includevarious optical elements. In some embodiments one or more lenses may beincluded, for example, to provide a desired focal length and field ofview for the image capture device. In some embodiments, image capturedevice 122 may be associated with a 6 mm lens or a 12 mm lens. In someembodiments, image capture device 122 may be configured to captureimages having a desired field-of-view (FOV) 202, as illustrated in FIG.2D. For example, image capture device 122 may be configured to have aregular FOV, such as within a range of 40 degrees to 56 degrees,including a 46 degree FOV, 50 degree FOV, 52 degree FOV, or greater.Alternatively, image capture device 122 may be configured to have anarrow FOV in the range of 23 to 40 degrees, such as a 28 degree FOV or36 degree FOV. In addition, image capture device 122 may be configuredto have a wide FOV in the range of 100 to 180 degrees. In someembodiments, image capture device 122 may include a wide angle bumpercamera or one with up to a 180 degree FOV.

The first image capture device 122 may acquire a plurality of firstimages relative to a scene associated with the vehicle 200. Each of theplurality of first images may be acquired as a series of image scanlines, which may be captured using a rolling shutter. Each scan line mayinclude a plurality of pixels.

The first image capture device 122 may have a scan rate associated withacquisition of each of the first series of image scan lines. The scanrate may refer to a rate at which an image sensor can acquire image dataassociated with each pixel included in a particular scan line.

Image capture devices 122, 124, and 126 may contain any suitable typeand number of image sensors, including CCD sensors or CMOS sensors, forexample. In one embodiment, a CMOS image sensor may be employed alongwith a rolling shutter, such that each pixel in a row is read one at atime, and scanning of the rows proceeds on a row-by-row basis until anentire image frame has been captured. In some embodiments, the rows maybe captured sequentially from top to bottom relative to the frame.

The use of a rolling shutter may result in pixels in different rowsbeing exposed and captured at different times, which may cause skew andother image artifacts in the captured image frame. On the other hand,when the image capture device 122 is configured to operate with a globalor synchronous shutter, all of the pixels may be exposed for the sameamount of time and during a common exposure period. As a result, theimage data in a frame collected from a system employing a global shutterrepresents a snapshot of the entire FOV (such as FOV 202) at aparticular time. In contrast, in a rolling shutter application, each rowin a frame is exposed and data is capture at different times. Thus,moving objects may appear distorted in an image capture device having arolling shutter. This phenomenon will be described in greater detailbelow.

The second image capture device 124 and the third image capturing device126 may be any type of image capture device. Like the first imagecapture device 122, each of image capture devices 124 and 126 mayinclude an optical axis. In one embodiment, each of image capturedevices 124 and 126 may include an Aptina M9V024 WVGA sensor with aglobal shutter. Alternatively, each of image capture devices 124 and 126may include a rolling shutter. Like image capture device 122, imagecapture devices 124 and 126 may be configured to include various lensesand optical elements. In some embodiments, lenses associated with imagecapture devices 124 and 126 may provide FOVs (such as FOVs 204 and 206)that are the same as, or narrower than, a FOV (such as FOV 202)associated with image capture device 122. For example, image capturedevices 124 and 126 may have FOVs of 40 degrees, 30 degrees, 26 degrees,23 degrees, 20 degrees, or less.

Image capture devices 124 and 126 may acquire a plurality of second andthird images relative to a scene associated with the vehicle 200. Eachof the plurality of second and third images may be acquired as a secondand third series of image scan lines, which may be captured using arolling shutter. Each scan line or row may have a plurality of pixels.Image capture devices 124 and 126 may have second and third scan ratesassociated with acquisition of each of image scan lines included in thesecond and third series.

Each image capture device 122, 124, and 126 may be positioned at anysuitable position and orientation relative to vehicle 200. The relativepositioning of the image capture devices 122, 124, and 126 may beselected to aid in fusing together the information acquired from theimage capture devices. For example, in some embodiments, a FOV (such asFOV 204) associated with image capture device 124 may overlap partiallyor fully with a FOV (such as FOV 202) associated with image capturedevice 122 and a FOV (such as FOV 206) associated with image capturedevice 126.

Image capture devices 122, 124, and 126 may be located on vehicle 200 atany suitable relative heights. In one instance, there may be a heightdifference between the image capture devices 122, 124, and 126, whichmay provide sufficient parallax information to enable stereo analysis.For example, as shown in FIG. 2A, the two image capture devices 122 and124 are at different heights. There may also be a lateral displacementdifference between image capture devices 122, 124, and 126, givingadditional parallax information for stereo analysis by processing unit110, for example. The difference in the lateral displacement may bedenoted by d_(x), as shown in FIGS. 2C and 2D. In some embodiments, foreor aft displacement (e.g., range displacement) may exist between imagecapture devices 122, 124, and 126. For example, image capture device 122may be located 0.5 to 2 meters or more behind image capture device 124and/or image capture device 126. This type of displacement may enableone of the image capture devices to cover potential blind spots of theother image capture device(s).

Image capture devices 122 may have any suitable resolution capability(e.g., number of pixels associated with the image sensor), and theresolution of the image sensor(s) associated with the image capturedevice 122 may be higher, lower, or the same as the resolution of theimage sensor(s) associated with image capture devices 124 and 126. Insome embodiments, the image sensor(s) associated with image capturedevice 122 and/or image capture devices 124 and 126 may have aresolution of 640×480, 1024×768, 1280×960, or any other suitableresolution.

The frame rate (e.g., the rate at which an image capture device acquiresa set of pixel data of one image frame before moving on to capture pixeldata associated with the next image frame) may be controllable. Theframe rate associated with image capture device 122 may be higher,lower, or the same as the frame rate associated with image capturedevices 124 and 126. The frame rate associated with image capturedevices 122, 124, and 126 may depend on a variety of factors that mayaffect the timing of the frame rate. For example, one or more of imagecapture devices 122, 124, and 126 may include a selectable pixel delayperiod imposed before or after acquisition of image data associated withone or more pixels of an image sensor in image capture device 122, 124,and/or 126. Generally, image data corresponding to each pixel may beacquired according to a clock rate for the device (e.g., one pixel perclock cycle). Additionally, in embodiments including a rolling shutter,one or more of image capture devices 122, 124, and 126 may include aselectable horizontal blanking period imposed before or afteracquisition of image data associated with a row of pixels of an imagesensor in image capture device 122, 124, and/or 126. Further, one ormore of image capture devices 122, 124, and/or 126 may include aselectable vertical blanking period imposed before or after acquisitionof image data associated with an image frame of image capture device122, 124, and 126.

These timing controls may enable synchronization of frame ratesassociated with image capture devices 122, 124, and 126, even where theline scan rates of each are different. Additionally, as will bediscussed in greater detail below, these selectable timing controls,among other factors (e.g., image sensor resolution, maximum line scanrates, etc.) may enable synchronization of image capture from an areawhere the FOV of image capture device 122 overlaps with one or more FOVsof image capture devices 124 and 126, even where the field of view ofimage capture device 122 is different from the FOVs of image capturedevices 124 and 126.

Frame rate timing in image capture device 122, 124, and 126 may dependon the resolution of the associated image sensors. For example, assumingsimilar line scan rates for both devices, if one device includes animage sensor having a resolution of 640×480 and another device includesan image sensor with a resolution of 1280×960, then more time will berequired to acquire a frame of image data from the sensor having thehigher resolution.

Another factor that may affect the timing of image data acquisition inimage capture devices 122, 124, and 126 is the maximum line scan rate.For example, acquisition of a row of image data from an image sensorincluded in image capture device 122, 124, and 126 will require someminimum amount of time. Assuming no pixel delay periods are added, thisminimum amount of time for acquisition of a row of image data will berelated to the maximum line scan rate for a particular device. Devicesthat offer higher maximum line scan rates have the potential to providehigher frame rates than devices with lower maximum line scan rates. Insome embodiments, one or more of image capture devices 124 and 126 mayhave a maximum line scan rate that is higher than a maximum line scanrate associated with image capture device 122. In some embodiments, themaximum line scan rate of image capture device 124 and/or 126 may be1.25, 1.5, 1.75, or 2 times or more than a maximum line scan rate ofimage capture device 122.

In another embodiment, image capture devices 122, 124, and 126 may havethe same maximum line scan rate, but image capture device 122 may beoperated at a scan rate less than or equal to its maximum scan rate. Thesystem may be configured such that one or more of image capture devices124 and 126 operate at a line scan rate that is equal to the line scanrate of image capture device 122. In other instances, the system may beconfigured such that the line scan rate of image capture device 124and/or image capture device 126 may be 1.25, 1.5, 1.75, or 2 times ormore than the line scan rate of image capture device 122.

In some embodiments, image capture devices 122, 124, and 126 may beasymmetric. That is, they may include cameras having different fields ofview (FOV) and focal lengths. The fields of view of image capturedevices 122, 124, and 126 may include any desired area relative to anenvironment of vehicle 200, for example. In some embodiments, one ormore of image capture devices 122, 124, and 126 may be configured toacquire image data from an environment in front of vehicle 200, behindvehicle 200, to the sides of vehicle 200, or combinations thereof.

Further, the focal length associated with each image capture device 122,124, and/or 126 may be selectable (e.g., by inclusion of appropriatelenses etc.) such that each device acquires images of objects at adesired distance range relative to vehicle 200. For example, in someembodiments image capture devices 122, 124, and 126 may acquire imagesof close-up objects within a few meters from the vehicle. Image capturedevices 122, 124, and 126 may also be configured to acquire images ofobjects at ranges more distant from the vehicle (e.g., 25 m, 50 m, 100m, 150 m, or more). Further, the focal lengths of image capture devices122, 124, and 126 may be selected such that one image capture device(e.g., image capture device 122) can acquire images of objectsrelatively close to the vehicle (e.g., within 10 m or within 20 m) whilethe other image capture devices (e.g., image capture devices 124 and126) can acquire images of more distant objects (e.g., greater than 20m, 50 m, 100 m, 150 m, etc.) from vehicle 200.

According to some embodiments, the FOV of one or more image capturedevices 122, 124, and 126 may have a wide angle. For example, it may beadvantageous to have a FOV of 140 degrees, especially for image capturedevices 122, 124, and 126 that may be used to capture images of the areain the vicinity of vehicle 200. For example, image capture device 122may be used to capture images of the area to the right or left ofvehicle 200 and, in such embodiments, it may be desirable for imagecapture device 122 to have a wide FOV (e.g., at least 140 degrees).

The field of view associated with each of image capture devices 122,124, and 126 may depend on the respective focal lengths. For example, asthe focal length increases, the corresponding field of view decreases.

Image capture devices 122, 124, and 126 may be configured to have anysuitable fields of view. In one particular example, image capture device122 may have a horizontal FOV of 46 degrees, image capture device 124may have a horizontal FOV of 23 degrees, and image capture device 126may have a horizontal FOV in between 23 and 46 degrees. In anotherinstance, image capture device 122 may have a horizontal FOV of 52degrees, image capture device 124 may have a horizontal FOV of 26degrees, and image capture device 126 may have a horizontal FOV inbetween 26 and 52 degrees. In some embodiments, a ratio of the FOV ofimage capture device 122 to the FOVs of image capture device 124 and/orimage capture device 126 may vary from 1.5 to 2.0. In other embodiments,this ratio may vary between 1.25 and 2.25.

System 100 may be configured so that a field of view of image capturedevice 122 overlaps, at least partially or fully, with a field of viewof image capture device 124 and/or image capture device 126. In someembodiments, system 100 may be configured such that the fields of viewof image capture devices 124 and 126, for example, fall within (e.g.,are narrower than) and share a common center with the field of view ofimage capture device 122. In other embodiments, the image capturedevices 122, 124, and 126 may capture adjacent FOVs or may have partialoverlap in their FOVs. In some embodiments, the fields of view of imagecapture devices 122, 124, and 126 may be aligned such that a center ofthe narrower FOV image capture devices 124 and/or 126 may be located ina lower half of the field of view of the wider FOV device 122.

FIG. 2F is a diagrammatic representation of exemplary vehicle controlsystems, consistent with the disclosed embodiments. As indicated in FIG.2F, vehicle 200 may include throttling system 220, braking system 230,and steering system 240. System 100 may provide inputs (e.g., controlsignals) to one or more of throttling system 220, braking system 230,and steering system 240 over one or more data links (e.g., any wiredand/or wireless link or links for transmitting data). For example, basedon analysis of images acquired by image capture devices 122, 124, and/or126, system 100 may provide control signals to one or more of throttlingsystem 220, braking system 230, and steering system 240 to navigatevehicle 200 (e.g., by causing an acceleration, a turn, a lane shift,etc.). Further, system 100 may receive inputs from one or more ofthrottling system 220, braking system 230, and steering system 24indicating operating conditions of vehicle 200 (e.g., speed, whethervehicle 200 is braking and/or turning, etc.). Further details areprovided in connection with FIGS. 4-7, below.

As shown in FIG. 3A, vehicle 200 may also include a user interface 170for interacting with a driver or a passenger of vehicle 200. Forexample, user interface 170 in a vehicle application may include a touchscreen 320, knobs 330, buttons 340, and a microphone 350. A driver orpassenger of vehicle 200 may also use handles (e.g., located on or nearthe steering column of vehicle 200 including, for example, turn signalhandles), buttons (e.g., located on the steering wheel of vehicle 200),and the like, to interact with system 100. In some embodiments,microphone 350 may be positioned adjacent to a rearview mirror 310.Similarly, in some embodiments, image capture device 122 may be locatednear rearview mirror 310. In some embodiments, user interface 170 mayalso include one or more speakers 360 (e.g., speakers of a vehicle audiosystem). For example, system 100 may provide various notifications(e.g., alerts) via speakers 360.

FIGS. 3B-3D are illustrations of an exemplary camera mount 370configured to be positioned behind a rearview mirror (e.g., rearviewmirror 310) and against a vehicle windshield, consistent with disclosedembodiments. As shown in FIG. 3B, camera mount 370 may include imagecapture devices 122, 124, and 126. Image capture devices 124 and 126 maybe positioned behind a glare shield 380, which may be flush against thevehicle windshield and include a composition of film and/oranti-reflective materials. For example, glare shield 380 may bepositioned such that it aligns against a vehicle windshield having amatching slope. In some embodiments, each of image capture devices 122,124, and 126 may be positioned behind glare shield 380, as depicted, forexample, in FIG. 3D. The disclosed embodiments are not limited to anyparticular configuration of image capture devices 122, 124, and 126,camera mount 370, and glare shield 380. FIG. 3C is an illustration ofcamera mount 370 shown in FIG. 3B from a front perspective.

As will be appreciated by a person skilled in the art having the benefitof this disclosure, numerous variations and/or modifications may be madeto the foregoing disclosed embodiments. For example, not all componentsare essential for the operation of system 100. Further, any componentmay be located in any appropriate part of system 100 and the componentsmay be rearranged into a variety of configurations while providing thefunctionality of the disclosed embodiments. Therefore, the foregoingconfigurations are examples and, regardless of the configurationsdiscussed above, system 100 can provide a wide range of functionality toanalyze the surroundings of vehicle 200 and navigate vehicle 200 inresponse to the analysis.

As discussed below in further detail and consistent with variousdisclosed embodiments, system 100 may provide a variety of featuresrelated to autonomous driving and/or driver assist technology. Forexample, system 100 may analyze image data, position data (e.g., GPSlocation information), map data, speed data, and/or data from sensorsincluded in vehicle 200. System 100 may collect the data for analysisfrom, for example, image acquisition unit 120, position sensor 130, andother sensors. Further, system 100 may analyze the collected data todetermine whether or not vehicle 200 should take a certain action, andthen automatically take the determined action without humanintervention. For example, when vehicle 200 navigates without humanintervention, system 100 may automatically control the braking,acceleration, and/or steering of vehicle 200 (e.g., by sending controlsignals to one or more of throttling system 220, braking system 230, andsteering system 240). Further, system 100 may analyze the collected dataand issue warnings and/or alerts to vehicle occupants based on theanalysis of the collected data. Additional details regarding the variousembodiments that are provided by system 100 are provided below.

Forward-Facing Multi-Imaging System

As discussed above, system 100 may provide drive assist functionalitythat uses a multi-camera system. The multi-camera system may use one ormore cameras facing in the forward direction of a vehicle. In otherembodiments, the multi-camera system may include one or more camerasfacing to the side of a vehicle or to the rear of the vehicle. In oneembodiment, for example, system 100 may use a two-camera imaging system,where a first camera and a second camera (e.g., image capture devices122 and 124) may be positioned at the front and/or the sides of avehicle (e.g., vehicle 200). The first camera may have a field of viewthat is greater than, less than, or partially overlapping with, thefield of view of the second camera. In addition, the first camera may beconnected to a first image processor to perform monocular image analysisof images provided by the first camera, and the second camera may beconnected to a second image processor to perform monocular imageanalysis of images provided by the second camera. The outputs (e.g.,processed information) of the first and second image processors may becombined. In some embodiments, the second image processor may receiveimages from both the first camera and second camera to perform stereoanalysis. In another embodiment, system 100 may use a three-cameraimaging system where each of the cameras has a different field of view.Such a system may, therefore, make decisions based on informationderived from objects located at varying distances both forward and tothe sides of the vehicle. References to monocular image analysis mayrefer to instances where image analysis is performed based on imagescaptured from a single point of view (e.g., from a single camera).Stereo image analysis may refer to instances where image analysis isperformed based on two or more images captured with one or morevariations of an image capture parameter. For example, captured imagessuitable for performing stereo image analysis may include imagescaptured: from two or more different positions, from different fields ofview, using different focal lengths, along with parallax information,etc.

For example, in one embodiment, system 100 may implement a three cameraconfiguration using image capture devices 122-126. In such aconfiguration, image capture device 122 may provide a narrow field ofview (e.g., 34 degrees, or other values selected from a range of about20 to 45 degrees, etc.), image capture device 124 may provide a widefield of view (e.g., 150 degrees or other values selected from a rangeof about 100 to about 180 degrees), and image capture device 126 mayprovide an intermediate field of view (e.g., 46 degrees or other valuesselected from a range of about 35 to about 60 degrees). In someembodiments, image capture device 126 may act as a main or primarycamera. Image capture devices 122-126 may be positioned behind rearviewmirror 310 and positioned substantially side-by-side (e.g., 6 cm apart).Further, in some embodiments, as discussed above, one or more of imagecapture devices 122-126 may be mounted behind glare shield 380 that isflush with the windshield of vehicle 200. Such shielding may act tominimize the impact of any reflections from inside the car on imagecapture devices 122-126.

In another embodiment, as discussed above in connection with FIGS. 3Band 3C, the wide field of view camera (e.g., image capture device 124 inthe above example) may be mounted lower than the narrow and main fieldof view cameras (e.g., image devices 122 and 126 in the above example).This configuration may provide a free line of sight from the wide fieldof view camera. To reduce reflections, the cameras may be mounted closeto the windshield of vehicle 200, and may include polarizers on thecameras to damp reflected light.

A three camera system may provide certain performance characteristics.For example, some embodiments may include an ability to validate thedetection of objects by one camera based on detection results fromanother camera. In the three camera configuration discussed above,processing unit 110 may include, for example, three processing devices(e.g., three EyeQ series of processor chips, as discussed above), witheach processing device dedicated to processing images captured by one ormore of image capture devices 122-126.

In a three camera system, a first processing device may receive imagesfrom both the main camera and the narrow field of view camera, andperform vision processing of the narrow FOV camera to, for example,detect other vehicles, pedestrians, lane marks, traffic signs, trafficlights, and other road objects. Further, the first processing device maycalculate a disparity of pixels between the images from the main cameraand the narrow camera and create a 3D reconstruction of the environmentof vehicle 200. The first processing device may then combine the 3Dreconstruction with 3D map data or with 3D information calculated basedon information from another camera.

The second processing device may receive images from main camera andperform vision processing to detect other vehicles, pedestrians, lanemarks, traffic signs, traffic lights, and other road objects.Additionally, the second processing device may calculate a cameradisplacement and, based on the displacement, calculate a disparity ofpixels between successive images and create a 3D reconstruction of thescene (e.g., a structure from motion). The second processing device maysend the structure from motion based 3D reconstruction to the firstprocessing device to be combined with the stereo 3D images.

The third processing device may receive images from the wide FOV cameraand process the images to detect vehicles, pedestrians, lane marks,traffic signs, traffic lights, and other road objects. The thirdprocessing device may further execute additional processing instructionsto analyze images to identify objects moving in the image, such asvehicles changing lanes, pedestrians, etc.

In some embodiments, having streams of image-based information capturedand processed independently may provide an opportunity for providingredundancy in the system. Such redundancy may include, for example,using a first image capture device and the images processed from thatdevice to validate and/or supplement information obtained by capturingand processing image information from at least a second image capturedevice.

In some embodiments, system 100 may use two image capture devices (e.g.,image capture devices 122 and 124) in providing navigation assistancefor vehicle 200 and use a third image capture device (e.g., imagecapture device 126) to provide redundancy and validate the analysis ofdata received from the other two image capture devices. For example, insuch a configuration, image capture devices 122 and 124 may provideimages for stereo analysis by system 100 for navigating vehicle 200,while image capture device 126 may provide images for monocular analysisby system 100 to provide redundancy and validation of informationobtained based on images captured from image capture device 122 and/orimage capture device 124. That is, image capture device 126 (and acorresponding processing device) may be considered to provide aredundant sub-system for providing a check on the analysis derived fromimage capture devices 122 and 124 (e.g., to provide an automaticemergency braking (AEB) system).

One of skill in the art will recognize that the above cameraconfigurations, camera placements, number of cameras, camera locations,etc., are examples only. These components and others described relativeto the overall system may be assembled and used in a variety ofdifferent configurations without departing from the scope of thedisclosed embodiments. Further details regarding usage of a multi-camerasystem to provide driver assist and/or autonomous vehicle functionalityfollow below.

FIG. 4 is an exemplary functional block diagram of memory 140 and/or150, which may be stored/programmed with instructions for performing oneor more operations consistent with the disclosed embodiments. Althoughthe following refers to memory 140, one of skill in the art willrecognize that instructions may be stored in memory 140 and/or 150.

As shown in FIG. 4, memory 140 may store a monocular image analysismodule 402, a stereo image analysis module 404, a velocity andacceleration module 406, and a navigational response module 408. Thedisclosed embodiments are not limited to any particular configuration ofmemory 140. Further, application processor 180 and/or image processor190 may execute the instructions stored in any of modules 402-408included in memory 140. One of skill in the art will understand thatreferences in the following discussions to processing unit 110 may referto application processor 180 and image processor 190 individually orcollectively. Accordingly, steps of any of the following processes maybe performed by one or more processing devices.

In one embodiment, monocular image analysis module 402 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs monocular image analysis of a set ofimages acquired by one of image capture devices 122, 124, and 126. Insome embodiments, processing unit 110 may combine information from a setof images with additional sensory information (e.g., information fromradar) to perform the monocular image analysis. As described inconnection with FIGS. 5A-5D below, monocular image analysis module 402may include instructions for detecting a set of features within the setof images, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, hazardous objects, and any otherfeature associated with an environment of a vehicle. Based on theanalysis, system 100 (e.g., via processing unit 110) may cause one ormore navigational responses in vehicle 200, such as a turn, a laneshift, a change in acceleration, and the like, as discussed below inconnection with navigational response module 408.

In one embodiment, stereo image analysis module 404 may storeinstructions (such as computer vision software) which, when executed byprocessing unit 110, performs stereo image analysis of first and secondsets of images acquired by a combination of image capture devicesselected from any of image capture devices 122, 124, and 126. In someembodiments, processing unit 110 may combine information from the firstand second sets of images with additional sensory information (e.g.,information from radar) to perform the stereo image analysis. Forexample, stereo image analysis module 404 may include instructions forperforming stereo image analysis based on a first set of images acquiredby image capture device 124 and a second set of images acquired by imagecapture device 126. As described in connection with FIG. 6 below, stereoimage analysis module 404 may include instructions for detecting a setof features within the first and second sets of images, such as lanemarkings, vehicles, pedestrians, road signs, highway exit ramps, trafficlights, hazardous objects, and the like. Based on the analysis,processing unit 110 may cause one or more navigational responses invehicle 200, such as a turn, a lane shift, a change in acceleration, andthe like, as discussed below in connection with navigational responsemodule 408.

In one embodiment, velocity and acceleration module 406 may storesoftware configured to analyze data received from one or more computingand electromechanical devices in vehicle 200 that are configured tocause a change in velocity and/or acceleration of vehicle 200. Forexample, processing unit 110 may execute instructions associated withvelocity and acceleration module 406 to calculate a target speed forvehicle 200 based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include, for example, a target position, velocity, and/oracceleration, the position and/or speed of vehicle 200 relative to anearby vehicle, pedestrian, or road object, position information forvehicle 200 relative to lane markings of the road, and the like. Inaddition, processing unit 110 may calculate a target speed for vehicle200 based on sensory input (e.g., information from radar) and input fromother systems of vehicle 200, such as throttling system 220, brakingsystem 230, and/or steering system 240 of vehicle 200. Based on thecalculated target speed, processing unit 110 may transmit electronicsignals to throttling system 220, braking system 230, and/or steeringsystem 240 of vehicle 200 to trigger a change in velocity and/oracceleration by, for example, physically depressing the brake or easingup off the accelerator of vehicle 200.

In one embodiment, navigational response module 408 may store softwareexecutable by processing unit 110 to determine a desired navigationalresponse based on data derived from execution of monocular imageanalysis module 402 and/or stereo image analysis module 404. Such datamay include position and speed information associated with nearbyvehicles, pedestrians, and road objects, target position information forvehicle 200, and the like. Additionally, in some embodiments, thenavigational response may be based (partially or fully) on map data, apredetermined position of vehicle 200, and/or a relative velocity or arelative acceleration between vehicle 200 and one or more objectsdetected from execution of monocular image analysis module 402 and/orstereo image analysis module 404. Navigational response module 408 mayalso determine a desired navigational response based on sensory input(e.g., information from radar) and inputs from other systems of vehicle200, such as throttling system 220, braking system 230, and steeringsystem 240 of vehicle 200. Based on the desired navigational response,processing unit 110 may transmit electronic signals to throttling system220, braking system 230, and steering system 240 of vehicle 200 totrigger a desired navigational response by, for example, turning thesteering wheel of vehicle 200 to achieve a rotation of a predeterminedangle. In some embodiments, processing unit 110 may use the output ofnavigational response module 408 (e.g., the desired navigationalresponse) as an input to execution of velocity and acceleration module406 for calculating a change in speed of vehicle 200.

FIG. 5A is a flowchart showing an exemplary process 500A for causing oneor more navigational responses based on monocular image analysis,consistent with disclosed embodiments. At step 510, processing unit 110may receive a plurality of images via data interface 128 betweenprocessing unit 110 and image acquisition unit 120. For instance, acamera included in image acquisition unit 120 (such as image capturedevice 122 having field of view 202) may capture a plurality of imagesof an area forward of vehicle 200 (or to the sides or rear of a vehicle,for example) and transmit them over a data connection (e.g., digital,wired, USB, wireless, Bluetooth, etc.) to processing unit 110.Processing unit 110 may execute monocular image analysis module 402 toanalyze the plurality of images at step 520, as described in furtherdetail in connection with FIGS. 5B-5D below. By performing the analysis,processing unit 110 may detect a set of features within the set ofimages, such as lane markings, vehicles, pedestrians, road signs,highway exit ramps, traffic lights, and the like.

Processing unit 110 may also execute monocular image analysis module 402to detect various road hazards at step 520, such as, for example, partsof a truck tire, fallen road signs, loose cargo, small animals, and thelike. Road hazards may vary in structure, shape, size, and color, whichmay make detection of such hazards more challenging. In someembodiments, processing unit 110 may execute monocular image analysismodule 402 to perform multi-frame analysis on the plurality of images todetect road hazards. For example, processing unit 110 may estimatecamera motion between consecutive image frames and calculate thedisparities in pixels between the frames to construct a 3D-map of theroad. Processing unit 110 may then use the 3D-map to detect the roadsurface, as well as hazards existing above the road surface.

At step 530, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 520 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration,and the like. In some embodiments, processing unit 110 may use dataderived from execution of velocity and acceleration module 406 to causethe one or more navigational responses. Additionally, multiplenavigational responses may occur simultaneously, in sequence, or anycombination thereof. For instance, processing unit 110 may cause vehicle200 to shift one lane over and then accelerate by, for example,sequentially transmitting control signals to steering system 240 andthrottling system 220 of vehicle 200. Alternatively, processing unit 110may cause vehicle 200 to brake while at the same time shifting lanes by,for example, simultaneously transmitting control signals to brakingsystem 230 and steering system 240 of vehicle 200.

FIG. 5B is a flowchart showing an exemplary process 500B for detectingone or more vehicles and/or pedestrians in a set of images, consistentwith disclosed embodiments. Processing unit 110 may execute monocularimage analysis module 402 to implement process 500B. At step 540,processing unit 110 may determine a set of candidate objectsrepresenting possible vehicles and/or pedestrians. For example,processing unit 110 may scan one or more images, compare the images toone or more predetermined patterns, and identify within each imagepossible locations that may contain objects of interest (e.g., vehicles,pedestrians, or portions thereof). The predetermined patterns may bedesigned in such a way to achieve a high rate of “false hits” and a lowrate of “misses.” For example, processing unit 110 may use a lowthreshold of similarity to predetermined patterns for identifyingcandidate objects as possible vehicles or pedestrians. Doing so mayallow processing unit 110 to reduce the probability of missing (e.g.,not identifying) a candidate object representing a vehicle orpedestrian.

At step 542, processing unit 110 may filter the set of candidate objectsto exclude certain candidates (e.g., irrelevant or less relevantobjects) based on classification criteria. Such criteria may be derivedfrom various properties associated with object types stored in adatabase (e.g., a database stored in memory 140). Properties may includeobject shape, dimensions, texture, position (e.g., relative to vehicle200), and the like. Thus, processing unit 110 may use one or more setsof criteria to reject false candidates from the set of candidateobjects.

At step 544, processing unit 110 may analyze multiple frames of imagesto determine whether objects in the set of candidate objects representvehicles and/or pedestrians. For example, processing unit 110 may tracka detected candidate object across consecutive frames and accumulateframe-by-frame data associated with the detected object (e.g., size,position relative to vehicle 200, etc.). Additionally, processing unit110 may estimate parameters for the detected object and compare theobject's frame-by-frame position data to a predicted position.

At step 546, processing unit 110 may construct a set of measurements forthe detected objects. Such measurements may include, for example,position, velocity, and acceleration values (relative to vehicle 200)associated with the detected objects. In some embodiments, processingunit 110 may construct the measurements based on estimation techniquesusing a series of time-based observations such as Kalman filters orlinear quadratic estimation (LQE), and/or based on available modelingdata for different object types (e.g., cars, trucks, pedestrians,bicycles, road signs, etc.). The Kalman filters may be based on ameasurement of an object's scale, where the scale measurement isproportional to a time to collision (e.g., the amount of time forvehicle 200 to reach the object). Thus, by performing steps 540-546,processing unit 110 may identify vehicles and pedestrians appearingwithin the set of captured images and derive information (e.g.,position, speed, size) associated with the vehicles and pedestrians.Based on the identification and the derived information, processing unit110 may cause one or more navigational responses in vehicle 200, asdescribed in connection with FIG. 5A, above.

At step 548, processing unit 110 may perform an optical flow analysis ofone or more images to reduce the probabilities of detecting a “falsehit” and missing a candidate object that represents a vehicle orpedestrian. The optical flow analysis may refer to, for example,analyzing motion patterns relative to vehicle 200 in the one or moreimages associated with other vehicles and pedestrians, and that aredistinct from road surface motion. Processing unit 110 may calculate themotion of candidate objects by observing the different positions of theobjects across multiple image frames, which are captured at differenttimes. Processing unit 110 may use the position and time values asinputs into mathematical models for calculating the motion of thecandidate objects. Thus, optical flow analysis may provide anothermethod of detecting vehicles and pedestrians that are nearby vehicle200. Processing unit 110 may perform optical flow analysis incombination with steps 540-546 to provide redundancy for detectingvehicles and pedestrians and increase the reliability of system 100.

FIG. 5C is a flowchart showing an exemplary process 500C for detectingroad marks and/or lane geometry information in a set of images,consistent with disclosed embodiments. Processing unit 110 may executemonocular image analysis module 402 to implement process 500C. At step550, processing unit 110 may detect a set of objects by scanning one ormore images. To detect segments of lane markings, lane geometryinformation, and other pertinent road marks, processing unit 110 mayfilter the set of objects to exclude those determined to be irrelevant(e.g., minor potholes, small rocks, etc.). At step 552, processing unit110 may group together the segments detected in step 550 belonging tothe same road mark or lane mark. Based on the grouping, processing unit110 may develop a model to represent the detected segments, such as amathematical model.

At step 554, processing unit 110 may construct a set of measurementsassociated with the detected segments. In some embodiments, processingunit 110 may create a projection of the detected segments from the imageplane onto the real-world plane. The projection may be characterizedusing a 3rd-degree polynomial having coefficients corresponding tophysical properties such as the position, slope, curvature, andcurvature derivative of the detected road. In generating the projection,processing unit 110 may take into account changes in the road surface,as well as pitch and roll rates associated with vehicle 200. Inaddition, processing unit 110 may model the road elevation by analyzingposition and motion cues present on the road surface. Further,processing unit 110 may estimate the pitch and roll rates associatedwith vehicle 200 by tracking a set of feature points in the one or moreimages.

At step 556, processing unit 110 may perform multi-frame analysis by,for example, tracking the detected segments across consecutive imageframes and accumulating frame-by-frame data associated with detectedsegments. As processing unit 110 performs multi-frame analysis, the setof measurements constructed at step 554 may become more reliable andassociated with an increasingly higher confidence level. Thus, byperforming steps 550-556, processing unit 110 may identify road marksappearing within the set of captured images and derive lane geometryinformation. Based on the identification and the derived information,processing unit 110 may cause one or more navigational responses invehicle 200, as described in connection with FIG. 5A, above.

At step 558, processing unit 110 may consider additional sources ofinformation to further develop a safety model for vehicle 200 in thecontext of its surroundings. Processing unit 110 may use the safetymodel to define a context in which system 100 may execute autonomouscontrol of vehicle 200 in a safe manner. To develop the safety model, insome embodiments, processing unit 110 may consider the position andmotion of other vehicles, the detected road edges and barriers, and/orgeneral road shape descriptions extracted from map data (such as datafrom map database 160). By considering additional sources ofinformation, processing unit 110 may provide redundancy for detectingroad marks and lane geometry and increase the reliability of system 100.

FIG. 5D is a flowchart showing an exemplary process 500D for detectingtraffic lights in a set of images, consistent with disclosedembodiments. Processing unit 110 may execute monocular image analysismodule 402 to implement process 500D. At step 560, processing unit 110may scan the set of images and identify objects appearing at locationsin the images likely to contain traffic lights. For example, processingunit 110 may filter the identified objects to construct a set ofcandidate objects, excluding those objects unlikely to correspond totraffic lights. The filtering may be done based on various propertiesassociated with traffic lights, such as shape, dimensions, texture,position (e.g., relative to vehicle 200), and the like. Such propertiesmay be based on multiple examples of traffic lights and traffic controlsignals and stored in a database. In some embodiments, processing unit110 may perform multi-frame analysis on the set of candidate objectsreflecting possible traffic lights. For example, processing unit 110 maytrack the candidate objects across consecutive image frames, estimatethe real-world position of the candidate objects, and filter out thoseobjects that are moving (which are unlikely to be traffic lights). Insome embodiments, processing unit 110 may perform color analysis on thecandidate objects and identify the relative position of the detectedcolors appearing inside possible traffic lights.

At step 562, processing unit 110 may analyze the geometry of a junction.The analysis may be based on any combination of: (i) the number of lanesdetected on either side of vehicle 200, (ii) markings (such as arrowmarks) detected on the road, and (iii) descriptions of the junctionextracted from map data (such as data from map database 160). Processingunit 110 may conduct the analysis using information derived fromexecution of monocular analysis module 402. In addition, processing unit110 may determine a correspondence between the traffic lights detectedat step 560 and the lanes appearing near vehicle 200.

As vehicle 200 approaches the junction, at step 564, processing unit 110may update the confidence level associated with the analyzed junctiongeometry and the detected traffic lights. For instance, the number oftraffic lights estimated to appear at the junction as compared with thenumber actually appearing at the junction may impact the confidencelevel. Thus, based on the confidence level, processing unit 110 maydelegate control to the driver of vehicle 200 in order to improve safetyconditions. By performing steps 560-564, processing unit 110 mayidentify traffic lights appearing within the set of captured images andanalyze junction geometry information. Based on the identification andthe analysis, processing unit 110 may cause one or more navigationalresponses in vehicle 200, as described in connection with FIG. 5A,above.

FIG. 5E is a flowchart showing an exemplary process 500E for causing oneor more navigational responses in vehicle 200 based on a vehicle path,consistent with the disclosed embodiments. At step 570, processing unit110 may construct an initial vehicle path associated with vehicle 200.The vehicle path may be represented using a set of points expressed incoordinates (x, z), and the distance d_(i) between two points in the setof points may fall in the range of 1 to 5 meters. In one embodiment,processing unit 110 may construct the initial vehicle path using twopolynomials, such as left and right road polynomials. Processing unit110 may calculate the geometric midpoint between the two polynomials andoffset each point included in the resultant vehicle path by apredetermined offset (e.g., a smart lane offset), if any (an offset ofzero may correspond to travel in the middle of a lane). The offset maybe in a direction perpendicular to a segment between any two points inthe vehicle path. In another embodiment, processing unit 110 may use onepolynomial and an estimated lane width to offset each point of thevehicle path by half the estimated lane width plus a predeterminedoffset (e.g., a smart lane offset).

At step 572, processing unit 110 may update the vehicle path constructedat step 570. Processing unit 110 may reconstruct the vehicle pathconstructed at step 570 using a higher resolution, such that thedistance d_(k) between two points in the set of points representing thevehicle path is less than the distance d_(i) described above. Forexample, the distance d_(k) may fall in the range of 0.1 to 0.3 meters.Processing unit 110 may reconstruct the vehicle path using a parabolicspline algorithm, which may yield a cumulative distance vector Scorresponding to the total length of the vehicle path (i.e., based onthe set of points representing the vehicle path).

At step 574, processing unit 110 may determine a look-ahead point(expressed in coordinates as (x_(l), z_(l))) based on the updatedvehicle path constructed at step 572. Processing unit 110 may extractthe look-ahead point from the cumulative distance vector S, and thelook-ahead point may be associated with a look-ahead distance andlook-ahead time. The look-ahead distance, which may have a lower boundranging from 10 to 20 meters, may be calculated as the product of thespeed of vehicle 200 and the look-ahead time. For example, as the speedof vehicle 200 decreases, the look-ahead distance may also decrease(e.g., until it reaches the lower bound). The look-ahead time, which mayrange from 0.5 to 1.5 seconds, may be inversely proportional to the gainof one or more control loops associated with causing a navigationalresponse in vehicle 200, such as the heading error tracking controlloop. For example, the gain of the heading error tracking control loopmay depend on the bandwidth of a yaw rate loop, a steering actuatorloop, car lateral dynamics, and the like. Thus, the higher the gain ofthe heading error tracking control loop, the lower the look-ahead time.

At step 576, processing unit 110 may determine a heading error and yawrate command based on the look-ahead point determined at step 574.Processing unit 110 may determine the heading error by calculating thearctangent of the look-ahead point, e.g., arctan (x_(l)/z_(l)).Processing unit 110 may determine the yaw rate command as the product ofthe heading error and a high-level control gain. The high-level controlgain may be equal to: (2/look-ahead time), if the look-ahead distance isnot at the lower bound. Otherwise, the high-level control gain may beequal to: (2*speed of vehicle 200/look-ahead distance).

FIG. 5F is a flowchart showing an exemplary process 500F for determiningwhether a leading vehicle is changing lanes, consistent with thedisclosed embodiments. At step 580, processing unit 110 may determinenavigation information associated with a leading vehicle (e.g., avehicle traveling ahead of vehicle 200). For example, processing unit110 may determine the position, velocity (e.g., direction and speed),and/or acceleration of the leading vehicle, using the techniquesdescribed in connection with FIGS. 5A and 5B, above. Processing unit 110may also determine one or more road polynomials, a look-ahead point(associated with vehicle 200), and/or a snail trail (e.g., a set ofpoints describing a path taken by the leading vehicle), using thetechniques described in connection with FIG. 5E, above.

At step 582, processing unit 110 may analyze the navigation informationdetermined at step 580. In one embodiment, processing unit 110 maycalculate the distance between a snail trail and a road polynomial(e.g., along the trail). If the variance of this distance along thetrail exceeds a predetermined threshold (for example, 0.1 to 0.2 meterson a straight road, 0.3 to 0.4 meters on a moderately curvy road, and0.5 to 0.6 meters on a road with sharp curves), processing unit 110 maydetermine that the leading vehicle is likely changing lanes. In the casewhere multiple vehicles are detected traveling ahead of vehicle 200,processing unit 110 may compare the snail trails associated with eachvehicle. Based on the comparison, processing unit 110 may determine thata vehicle whose snail trail does not match with the snail trails of theother vehicles is likely changing lanes. Processing unit 110 mayadditionally compare the curvature of the snail trail (associated withthe leading vehicle) with the expected curvature of the road segment inwhich the leading vehicle is traveling. The expected curvature may beextracted from map data (e.g., data from map database 160), from roadpolynomials, from other vehicles' snail trails, from prior knowledgeabout the road, and the like. If the difference in curvature of thesnail trail and the expected curvature of the road segment exceeds apredetermined threshold, processing unit 110 may determine that theleading vehicle is likely changing lanes.

In another embodiment, processing unit 110 may compare the leadingvehicle's instantaneous position with the look-ahead point (associatedwith vehicle 200) over a specific period of time (e.g., 0.5 to 1.5seconds). If the distance between the leading vehicle's instantaneousposition and the look-ahead point varies during the specific period oftime, and the cumulative sum of variation exceeds a predeterminedthreshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8meters on a moderately curvy road, and 1.3 to 1.7 meters on a road withsharp curves), processing unit 110 may determine that the leadingvehicle is likely changing lanes. In another embodiment, processing unit110 may analyze the geometry of the snail trail by comparing the lateraldistance traveled along the trail with the expected curvature of thesnail trail. The expected radius of curvature may be determinedaccording to the calculation: (δ_(z) ²+δ_(x) ²)/2/(δ_(x)), where δ_(x)represents the lateral distance traveled and δ_(z) represents thelongitudinal distance traveled. If the difference between the lateraldistance traveled and the expected curvature exceeds a predeterminedthreshold (e.g., 500 to 700 meters), processing unit 110 may determinethat the leading vehicle is likely changing lanes. In anotherembodiment, processing unit 110 may analyze the position of the leadingvehicle. If the position of the leading vehicle obscures a roadpolynomial (e.g., the leading vehicle is overlaid on top of the roadpolynomial), then processing unit 110 may determine that the leadingvehicle is likely changing lanes. In the case where the position of theleading vehicle is such that, another vehicle is detected ahead of theleading vehicle and the snail trails of the two vehicles are notparallel, processing unit 110 may determine that the (closer) leadingvehicle is likely changing lanes.

At step 584, processing unit 110 may determine whether or not leadingvehicle 200 is changing lanes based on the analysis performed at step582. For example, processing unit 110 may make the determination basedon a weighted average of the individual analyses performed at step 582.Under such a scheme, for example, a decision by processing unit 110 thatthe leading vehicle is likely changing lanes based on a particular typeof analysis may be assigned a value of “1” (and “0” to represent adetermination that the leading vehicle is not likely changing lanes).Different analyses performed at step 582 may be assigned differentweights, and the disclosed embodiments are not limited to any particularcombination of analyses and weights.

FIG. 6 is a flowchart showing an exemplary process 600 for causing oneor more navigational responses based on stereo image analysis,consistent with disclosed embodiments. At step 610, processing unit 110may receive a first and second plurality of images via data interface128. For example, cameras included in image acquisition unit 120 (suchas image capture devices 122 and 124 having fields of view 202 and 204)may capture a first and second plurality of images of an area forward ofvehicle 200 and transmit them over a digital connection (e.g., USB,wireless, Bluetooth, etc.) to processing unit 110. In some embodiments,processing unit 110 may receive the first and second plurality of imagesvia two or more data interfaces. The disclosed embodiments are notlimited to any particular data interface configurations or protocols.

At step 620, processing unit 110 may execute stereo image analysismodule 404 to perform stereo image analysis of the first and secondplurality of images to create a 3D map of the road in front of thevehicle and detect features within the images, such as lane markings,vehicles, pedestrians, road signs, highway exit ramps, traffic lights,road hazards, and the like. Stereo image analysis may be performed in amanner similar to the steps described in connection with FIGS. 5A-5D,above. For example, processing unit 110 may execute stereo imageanalysis module 404 to detect candidate objects (e.g., vehicles,pedestrians, road marks, traffic lights, road hazards, etc.) within thefirst and second plurality of images, filter out a subset of thecandidate objects based on various criteria, and perform multi-frameanalysis, construct measurements, and determine a confidence level forthe remaining candidate objects. In performing the steps above,processing unit 110 may consider information from both the first andsecond plurality of images, rather than information from one set ofimages alone. For example, processing unit 110 may analyze thedifferences in pixel-level data (or other data subsets from among thetwo streams of captured images) for a candidate object appearing in boththe first and second plurality of images. As another example, processingunit 110 may estimate a position and/or velocity of a candidate object(e.g., relative to vehicle 200) by observing that the object appears inone of the plurality of images but not the other or relative to otherdifferences that may exist relative to objects appearing if the twoimage streams. For example, position, velocity, and/or accelerationrelative to vehicle 200 may be determined based on trajectories,positions, movement characteristics, etc. of features associated with anobject appearing in one or both of the image streams.

At step 630, processing unit 110 may execute navigational responsemodule 408 to cause one or more navigational responses in vehicle 200based on the analysis performed at step 620 and the techniques asdescribed above in connection with FIG. 4. Navigational responses mayinclude, for example, a turn, a lane shift, a change in acceleration, achange in velocity, braking, and the like. In some embodiments,processing unit 110 may use data derived from execution of velocity andacceleration module 406 to cause the one or more navigational responses.Additionally, multiple navigational responses may occur simultaneously,in sequence, or any combination thereof.

FIG. 7 is a flowchart showing an exemplary process 700 for causing oneor more navigational responses based on an analysis of three sets ofimages, consistent with disclosed embodiments. At step 710, processingunit 110 may receive a first, second, and third plurality of images viadata interface 128. For instance, cameras included in image acquisitionunit 120 (such as image capture devices 122, 124, and 126 having fieldsof view 202, 204, and 206) may capture a first, second, and thirdplurality of images of an area forward and/or to the side of vehicle 200and transmit them over a digital connection (e.g., USB, wireless,Bluetooth, etc.) to processing unit 110. In some embodiments, processingunit 110 may receive the first, second, and third plurality of imagesvia three or more data interfaces. For example, each of image capturedevices 122, 124, 126 may have an associated data interface forcommunicating data to processing unit 110. The disclosed embodiments arenot limited to any particular data interface configurations orprotocols.

At step 720, processing unit 110 may analyze the first, second, andthird plurality of images to detect features within the images, such aslane markings, vehicles, pedestrians, road signs, highway exit ramps,traffic lights, road hazards, and the like. The analysis may beperformed in a manner similar to the steps described in connection withFIGS. 5A-5D and 6, above. For instance, processing unit 110 may performmonocular image analysis (e.g., via execution of monocular imageanalysis module 402 and based on the steps described in connection withFIGS. 5A-5D, above) on each of the first, second, and third plurality ofimages. Alternatively, processing unit 110 may perform stereo imageanalysis (e.g., via execution of stereo image analysis module 404 andbased on the steps described in connection with FIG. 6, above) on thefirst and second plurality of images, the second and third plurality ofimages, and/or the first and third plurality of images. The processedinformation corresponding to the analysis of the first, second, and/orthird plurality of images may be combined. In some embodiments,processing unit 110 may perform a combination of monocular and stereoimage analyses. For example, processing unit 110 may perform monocularimage analysis (e.g., via execution of monocular image analysis module402) on the first plurality of images and stereo image analysis (e.g.,via execution of stereo image analysis module 404) on the second andthird plurality of images. The configuration of image capture devices122, 124, and 126—including their respective locations and fields ofview 202, 204, and 206—may influence the types of analyses conducted onthe first, second, and third plurality of images. The disclosedembodiments are not limited to a particular configuration of imagecapture devices 122, 124, and 126, or the types of analyses conducted onthe first, second, and third plurality of images.

In some embodiments, processing unit 110 may perform testing on system100 based on the images acquired and analyzed at steps 710 and 720. Suchtesting may provide an indicator of the overall performance of system100 for certain configurations of image capture devices 122, 124, and126. For example, processing unit 110 may determine the proportion of“false hits” (e.g., cases where system 100 incorrectly determined thepresence of a vehicle or pedestrian) and “misses.”

At step 730, processing unit 110 may cause one or more navigationalresponses in vehicle 200 based on information derived from two of thefirst, second, and third plurality of images. Selection of two of thefirst, second, and third plurality of images may depend on variousfactors, such as, for example, the number, types, and sizes of objectsdetected in each of the plurality of images. Processing unit 110 mayalso make the selection based on image quality and resolution, theeffective field of view reflected in the images, the number of capturedframes, the extent to which one or more objects of interest actuallyappear in the frames (e.g., the percentage of frames in which an objectappears, the proportion of the object that appears in each such frame,etc.), and the like.

In some embodiments, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images bydetermining the extent to which information derived from one imagesource is consistent with information derived from other image sources.For example, processing unit 110 may combine the processed informationderived from each of image capture devices 122, 124, and 126 (whether bymonocular analysis, stereo analysis, or any combination of the two) anddetermine visual indicators (e.g., lane markings, a detected vehicle andits location and/or path, a detected traffic light, etc.) that areconsistent across the images captured from each of image capture devices122, 124, and 126. Processing unit 110 may also exclude information thatis inconsistent across the captured images (e.g., a vehicle changinglanes, a lane model indicating a vehicle that is too close to vehicle200, etc.). Thus, processing unit 110 may select information derivedfrom two of the first, second, and third plurality of images based onthe determinations of consistent and inconsistent information.

Navigational responses may include, for example, a turn, a lane shift, achange in acceleration, and the like. Processing unit 110 may cause theone or more navigational responses based on the analysis performed atstep 720 and the techniques as described above in connection with FIG.4. Processing unit 110 may also use data derived from execution ofvelocity and acceleration module 406 to cause the one or morenavigational responses. In some embodiments, processing unit 110 maycause the one or more navigational responses based on a relativeposition, relative velocity, and/or relative acceleration betweenvehicle 200 and an object detected within any of the first, second, andthird plurality of images. Multiple navigational responses may occursimultaneously, in sequence, or any combination thereof.

Object Detecting System

In some embodiments, vehicle 200 may include an object detecting systemconfigured to detect an object in front of vehicle 200 and in somecircumstances, cause vehicle 200 to brake, e.g., using braking system230, based on the detection of the object. For example, the objectdetecting system may cause vehicle 200 to brake automatically, orautonomously, without or with limited driver intervention. The automaticor autonomous object detecting and braking system may or may not allow adriver to intervene to take over control of braking system 230.

The object detecting system may include a plurality of other systemsdiscussed above. For example, the object detecting and braking systemmay include system 100, braking system 230, and one or more of imagecapture devices 122, 124, and 126. The one or more of image capturedevices 122, 124, and 126 may acquire one or more images of anenvironment or an area in front of vehicle 200, which may include anobject, such as another vehicle, a pedestrian, etc. Processing unit 110may compare at least two images to obtain optical flow information,which may include a vector field formed by a plurality of vectors (e.g.,displacement vectors) each representing a displacement between a firstpixel in the first image and a corresponding second pixel in the secondimage. Based on the optical flow information, processing unit 110 maydetect an object, such as an upright object in the images, and mayverify the structure of the object. The upright object may have a low(e.g., short) time to collision with vehicle 200. Thus, the uprightobject may also be referred to as a low time-to-collision uprightobject. In some embodiments, processing unit may search for a region ofcoherent expansion that is a set of pixels in, e.g., the first image,for which there exists a common focus of expansion and a common scalemagnitude such that the set of pixels satisfy a relationship betweenpixel positions, displacement vectors, the common focus of expansion,and the common scale magnitude. The region of coherent expansion mayrepresent a low time-to-collision upright object. The scale magnitudemay be defined as a magnitude of a displacement vector associated withthe pixel (if denoted as d) divided by a distance (if denoted as r) fromthe pixel to the focus of expansion, i.e., scale magnitude=d/r. Thescale magnitude may also be referred to as the expansion rate.

In some embodiments, processing unit 110 may calculate a plurality ofcombinations of possible scale magnitudes and possible focus ofexpansions for a plurality of pixels from at least one of the firstimage and the second image. The plurality of pixels may include the setof pixels to be searched for. In some embodiments, processing unit 110may determine the common focus of expansion and the common scalemagnitude based on a distribution of the plurality of combinations ofpossible scale magnitudes and possible focus of expansions. Theplurality of combinations of possible scale magnitudes and possiblefocus of expansions may be distributed in a three-dimensional space. Insome embodiments, processing unit 110 may determine the common focus ofexpansion and common scale magnitude using a suitable clusteringtechnique, such as one that may determine the center of mass (e.g.,center of density) about the distribution. Processing unit 110 maydetermine from the distribution that the density of distribution arounda particular pair of scale magnitude and focus of expansion is higherthan a predetermined threshold, and may select that particular pair ofscale magnitude and focus of expansion as the common scale magnitude andcommon focus of expansion.

Processing unit 110 may select the region of coherent expansion (i.e.,the set of pixels) based on the common focus of expansion and commonscale magnitude, wherein the set of pixels satisfy the relationshipbetween pixel positions, displacement vectors, the common focus ofexpansion, and the common scale magnitude. The set of pixels may beselected from the first image or the second image. Based on the selectedset of pixels (or the region of coherent expansion), processing unit 110may identify the presence of a substantially upright lowtime-to-collision object in the first image (or the second image). Forexample, the set of pixels may show a portion of the substantiallyupright low time-to-collision object. Processing unit 110 may identifysuch a low time-to-collision upright object regardless of whether theobject is static relative to the road, and/or regardless of the movingdirection of the object, and/or regardless of whether the object has alow (e.g., short) time to collision with vehicle 200, as long as theobject has a relative speed as compared to vehicle 200 such that thereexists a focus of expansion for the object. The focus of expansion maynot exist for the object when the object appears static in an imageplane relative to vehicle 200 because there would be no expansion orshrinking in the optical flow field.

Processing unit 110 may further determine that the identified object isa crossing object (which may also be referred to as a crossing-to-impactobject hereinafter) that is moving at a constant speed in a directioncrossing the travel direction of vehicle 200, and which would likelycollide with vehicle 200 should vehicle 200 and the crossing objectcontinue to move at their respective constant speeds. The crossingobject may be a low time-to-collision upright object. Processing unit110 may determine whether a horizontal coordinate of the focus ofexpansion of the crossing object stays around a center in the horizontaldimension of the crossing object during a period of time. This mayindicate that a viewing angle from a camera installed on vehicle 200 isconstant. When a viewing angle is constant, if the crossing object moveslinearly at a constant speed, and vehicle 200 moves linearly at aconstant speed, they may collide at certain point. Thus, processing unit110 may determine whether crossing object and vehicle 200 may collidebased on a determination of whether the horizontal coordinate of thefocus of expansion of the crossing object stays around a center in thehorizontal dimension of the crossing object during the period of time.When processing unit 110 determines that the horizontal coordinate ofthe focus of expansion of the crossing object stays around a center inthe horizontal dimension of the crossing object during the period oftime, processing unit may cause vehicle 200 to brake using, e.g.,braking system 230. In some embodiments, the braking may be autonomouswithout driver intervention.

FIG. 8 is a diagrammatic representation of an exemplary vehicle 200including an object detecting system (or referred to as an emergencybraking decisioning system). The object detecting system may includesystem 100 and braking system 230. The object detecting system may alsoinclude at least one of image capture devices 122 and 124 (and 126,which is not shown in FIG. 8). The object detecting system may includeprocessing unit 110, which may be part of system 100. At least one ofimage capture devices 122 and 124 may acquire one or a plurality ofimages of an area including an object in front of vehicle 200. Theobject may be another vehicle, such as a vehicle 800 that may betraveling in front of vehicle 200, or a pedestrian, an animal, etc. Asshown in FIG. 8, in some embodiments, vehicle 800 may be traveling in adirection that is parallel to a traveling direction of vehicle 200. Aparallel direction may include a situation in which vehicle 800 istraveling in the same direction as and in alignment or substantially inalignment with vehicle 200 (e.g., vehicle 800 traveling in front ofvehicle 200), as shown in FIG. 8. A parallel direction may also includea situation in which vehicle 800 is traveling in an opposite directionrelative to vehicle 200 and in alignment with vehicle 200 (e.g., vehicle800 moving toward vehicle 200 in a direction that is 180 degrees, orsubstantially 180 degrees, opposite to the traveling direction ofvehicle 200). A parallel direction may also include a situation in whichvehicle 800 is not traveling in alignment with vehicle 200, but istraveling in a direction parallel to the traveling direction of vehicle200 (e.g., vehicle 800 traveling on a front side of vehicle 200 at anoffset distance).

In some embodiments, the object may be a pedestrian who may be standingin the road, or walking or running across the road in front of vehicle200. The area included in the images may include other objects, such asa road surface, a house, a wall, a sign, etc., which may form a staticenvironment. When an object is not traveling in a direction parallel tothe traveling direction of vehicle 200, the traveling direction of theobject may transverse or cross the traveling direction of vehicle 200with an angle that is neither zero (or substantially zero) nor 180degrees (or substantially 180 degrees). The images may include bothmoving and/or static objects.

Processing unit 110 may communicate with braking system 230 and at leastone of image capture devices 122 and 124. For example, processing unit110 may receive images from at least one of image capture devices 122and 124, and may process the images using various methods to detect theobject (e.g., another vehicle, pedestrian, or animal, etc.). Althoughtwo image capture devices 122 and 124 are shown in FIG. 8, the use ofadditional image capture devices (e.g., 3, 4, 5, etc., image capturedevices) is consistent with the disclosed embodiments.

FIG. 9 is an exemplary block diagram of memory 140 or 150 that may storeinstructions for performing one or more operations for detecting anobject in front of vehicle 200 and/or causing vehicle 200 to brake basedon the detection. As shown in FIG. 9, memory 140 or 150 may store one ormore modules for performing the operations, as described herein. Forexample, memory 140 or 150 may store an image analysis module 900configured to analyze the images. Memory 140 or 150 may store a brakingmodule 905 configured to cause vehicle 200 to brake by controlling,e.g., braking system 230. Application processor 180 and/or imageprocessor 190 may execute the instructions stored in any of modules 900and 905 included in memory 140 or 150. One of skill in the art willunderstand that references in the following discussions to processingunit 110 may refer to application processor 180 and image processor 190individually or collectively. Accordingly, steps of any of the followingprocesses may be performed by one or more processing devices.

Image analysis module 900 may store instructions which, when executed byprocessing unit 110, may perform an image analysis to detect an objecttraveling in front of vehicle 200 (e.g., vehicle 800 or a pedestriancrossing the road). Image analysis module 900 may analyze at least twoimages acquired by at least one of image capture devices 122 and 124.For example, image analysis module 900 may compare at least a firstimage to a second image to obtain optical flow information. The opticalflow information may include an optical flow field formed by a pluralityof displacement vectors between each first pixel in the first image andeach corresponding second pixel in the second image. Image analysismodule 900 may search for a region of coherent expansion that is a setof pixels in at least one of the first image and the second image, forwhich there exists a common focus of expansion and a common scalemagnitude such that the set of pixels satisfy a relationship betweenpixel positions, displacement vectors, the common focus of expansion,and the common scale magnitude. Image analysis module 900 may furtheridentify presence of a substantially upright object in at least one ofthe first image and the second image based on the set of pixels.

In some embodiments, image analysis module 900 may store instructionswhich, when executed by processing unit 110, may perform an imageanalysis to detect an object crossing the road in front of vehicle 200,which would likely collide with vehicle 200 if vehicle 200 and theobject continue to move linearly (e.g., in a straight line) at theirrespective constant speeds in their respective directions that crosseach other at an angle which is neither zero nor 180 degrees. Imageanalysis module 900 may determine whether a horizontal coordinate of thefocus of expansion stays around a center in the horizontal dimension ofthe crossing object during a period of time.

Braking module 905 may store instructions which, when executed byprocessing unit 110, may cause vehicle 200 to brake using braking system230. For example, when image analysis module 900 determines that ahorizontal coordinate of the focus of expansion of the crossing objectstays around a center in the horizontal dimension of the crossing objectduring a period of time, braking module 905 may cause vehicle 200 tobrake to avoid collision with the crossing object.

FIG. 10A is an exemplary image of an area showing vectors associatedwith pixels. Although only one image of a vehicle 1000 is shown forillustration purposes, it is understood that a plurality of image may beacquired by one of the image capture devices 122-126 at a time intervalafter the first image is acquired. Processing unit 110 may compare afirst image to a second image to obtain optical flow informationincluding a vector field formed by a plurality of vectors, each vectorrepresenting displacement (or movement) associated with each first pixelin the first image and each corresponding second pixel in the secondimage. The vectors may also be referred to as displacement vectors. Inthe example displacement vector field shown in FIG. 10A, coordinates ofthree pixels in the first image are shown, (x1, y1), (x2, y2), and (x3,y3). Displacement vectors are shown as arrows. Coordinates (u1, v1),(u2, v2), and (u3, v3) may represent horizontal and verticaldisplacements associated with each displacement vector, or may representcoordinates of corresponding second pixels on the second image in someembodiments.

Processing unit 110 may search for a region of coherent expansion thatis a set of pixels in at least one of the first image and the secondimage, for which there exists a common focus of expansion and a commonscale magnitude such that the set of pixels satisfy a relationshipbetween pixel positions, displacement vectors, the common focus ofexpansion, and the common scale magnitude. The relationship may beexpressed as equations (1) and (2) for an ith pixel in the first image(or in the second image) having coordinates (xi, yi), where i is ainteger number:SS*(x _(i)−FOE_(x))=u _(i)  (1)SS*(y _(i)−FOE_(y))=v _(i)  (2)

Where SS is the scale magnitude, FOE_(x) and FOE_(y) are coordinates ofthe focus of expansion. Because SS, FOE_(x), and FOE_(y) are unknownvariables, there may be an infinite number of possible solutions forequations (1) and (2). The possible solutions of equations (1) and (2)may be distributed in a three-dimensional cube space shown in FIG. 10B(which may represent a portion of the infinite space), where the x axisrepresents FOE_(x), and the y-axis represents FOE_(y), and the z-axisrepresents scale magnitude SS. There may exist a set of pixels among thepixels of the first image (or the second image) for which there exists apair of common scale magnitude SS0 and common focus of expansion(FOE_(x0), FOE_(y0)) such that all or most of the set of pixels satisfythe relationship represented by equations (1) and (2) with SS beingreplaced by common scale magnitude SS0 and FOE_(x) and FOE_(y) beingreplaced by FOE_(x0), FOE_(y0) of the common focus of expansion. Thisset of pixels may be referred to a region of coherent expansion. Thepixels in the region of coherent expansion may share the same commonscale magnitude and the same focus of expansion.

A plurality of methods may be used to determine a common focus ofexpansion from the plurality of focuses of expansion. For example, aclustering technique may be used to determine the standard deviation ofthe distribution, the center of mass, or the center of the density ofdistribution. Such information obtained using the clustering techniquemay be used to determine the common scale magnitudes, SS0, and thecommon focus of expansion (FOE_(x0), FOE_(y0)).

In one embodiment, the three-dimensional cube space shown in FIG. 10Bmay be divided into a plurality of cells, each cell corresponding to asmall range corresponding to a pair of (SS_(i), FOE_(xi), FOE_(yi)).Processing unit 110 may place the possible solutions into the cells andcount the number of solutions within the cells. Processing unit 110 maydetermine which cell has the largest number of solutions. In oneembodiment, the cell has the largest number of solutions may be used todetermine the common focus of expansion and the common scale magnitude.

In another embodiment, processing unit 110 may one variable, such as oneof the scale magnitude SS, FOE_(x), or FOE_(y), and solve the equations(1) and (2) for possible solutions for each pixel. Processing unit 110may repeat this for all or a plurality of pixels in the first image togenerate a plurality of possible solutions. The plurality of possiblesolutions (SS, FOE_(x), FOE_(y)) may be arranged into a list or anarray. Processing unit 110 may determine a distribution of the possiblesolutions using, e.g., a clustering technique, to find a denseclustering. For example, if the density around a particular solution(SS, FOE_(x), FOE_(y)) is higher than a predetermined threshold,processing unit 110 may use that particular solution as the common focusof expansion and common scale magnitude for the plurality of pixels inthe first image.

In some embodiments, processing unit 110 may determine whether thepossible solutions “agree” with each other, e.g., whether all or most ofthe solutions or more than a predetermined number of solutions arelocated within a predetermined small space in the three-dimensional cubespace shown in FIG. 10B. If the possible solutions “agree” with eachother, processing unit 110 may determine the common focus of expansionand the commons scale magnitude based on the possible solutions withinthe predetermined space (e.g., taking the average of the all solutionswithin the predetermined space as the common scale magnitude and thecommon focus of expansion).

Based on the common focus of expansion and the common scale magnitude,processing unit 110 may identify the set of pixels that satisfy therelationship represented by equations (1) and (2) with the common scalemagnitude and common focus of expansion. In other words, processing unit110 may identify the set of pixels that share the common scale magnitudeand the common focus of expansion. Processing unit 110 may detect thepresence of an object, such as an upright object low time-to-collisionobject (e.g., a back door of a van shown in FIG. 10C) based on theidentified set of pixels. For example, the identified set of pixels mayshow an image of a substantial portion of the upright object (e.g., theback door of the van), as shown in FIG. 10C.

FIG. 11 is a flowchart showing an exemplary process for detectingpresence of an object in front of vehicle 200. Method 1100 may includeacquiring a plurality of images of an area in front of vehicle 200 (step1110). For example, at least one of image capture devices 122 and 124may acquire one or more images of an area in front of vehicle 200. Thearea may include an object, such as another vehicle or a pedestrian. Thearea may include other static objects, such as a sign, a house, amountain, a road surface, etc., which may form a static environment.

Method 1100 may include receiving the plurality of images (step 1120).For example, processing unit 110 may receive the plurality of imagesfrom at least one of the image capture devices 122 and 124. Method 1100may also include comparing a first image to a second image to determinedisplacement vectors between pixels of the first image and pixels of thesecond image (step 1130). The first and second images are from theplurality of images, and may be acquired at a predetermined timeinterval, such as 0.01 second, 0.02 second, etc. Processing unit 110 maycompare the first image and second image to obtain optical flowinformation, which may include displacement vectors between pixels ofthe first image and pixels of the second image. A displacement vectorindicates a direction and a magnitude of displacement between a firstpixel in the first image and a second pixel in the second image. Thedisplacement vectors may converge into one or more common convergingpoints, which may also be referred to as common focus of expansions.Processing unit 110 may determine the common focus of expansion based onany of the methods disclosed herein.

Method 1100 may further include searching for a region of coherentexpansion that is a set of pixels in at least one of the first image andthe second image, for which there exists a common focus of expansion anda common scale magnitude such that the set of pixels satisfy arelationship between pixel positions, displacement vectors, the commonfocus of expansion, and the common scale magnitude (step 1140). Forexample, processing unit 110 may search for the set of pixels thatsatisfy the relationship represented by equations (1) and (2) with thecommon focus of expansion and the common scale magnitude. Based on theset of pixels, processing unit 110 may identify the presence of asubstantially upright object in at least one of the first image and thesecond image (step 1150). The upright object may be lowtime-to-collision object, such as a moving vehicle or pedestrian withwhich vehicle 200 may collision at a future time.

FIG. 12A is an image of a static environment captured by at least one ofimage capture devices 122 and 124 installed on vehicle 200. The staticenvironment may include a road, one or more trees along the road, and aportion of the sky. FIG. 12B shows a three-dimensional cube spaceillustrating the relationship between the focus of expansion and thescale magnitude. The cube space shows a space where possible solutionsof (SS, FOE_(x), FOE_(y)) may be distributed. As shown in FIG. 12B,distribution of the scale magnitudes and focus of expansions ofdifferent objects in the static environment may be represented by acylindrical shape 1200 (or a straight line; the cylindrical shape isonly for illustration purposes). Different objects in the image of thestatic environment may have different distances to the camera on vehicle200. Thus, the scale magnitudes may be different for different objectsin the image of the static environment. As indicated by the cylindricalshape 1200 in FIG. 12B, objects in the static environment share the samefocus of expansion.

FIG. 13A is an image of a environment having another vehicle 1300 infront of vehicle 200. The object vehicle 1300 may be static relative tothe road or may be moving in a direction parallel to the travelingdirection of vehicle 200. As shown in FIG. 13B, the distribution of thescale magnitude and focus of expansion of different objects in thestatic environment may be represented by a cylindrical shape 1310. Thefocus of expansion of object vehicle 1300 may be represented by thecircle 1320 centered on the cylindrical shape 1310 at a certain scalemagnitude. In other words, the focus of expansion of object vehicle 1300is the same as the focus of expansion of the static environment.

FIG. 13C is an image of a environment having another object vehicle 1350traveling longitudinally in front of and in alignment with vehicle 200.As shown in FIG. 13D, the distribution of scale magnitudes and focus ofexpansions for different objects in the static environment may berepresented by a cylindrical shape 1360. The focus of expansion ofobject vehicle 1350 may be represented by a circle 1370 centered on thecylindrical shape 1360 at a certain scale magnitude. In other words, thefocus of expansion of object vehicle 1350 is the same as the focus ofexpansion of the static environment.

FIG. 14A is an image of an environment superimposed with an exemplaryoptical flow field (e.g., the displacement vector field). The image ofthe environment may include an object vehicle 1410 and a pedestrian1420, who may be crossing the road or standing still on the road. Anoptical flow field having a plurality of vectors is superimposed to thefigure to indicate that an optical flow field may be obtained from atleast two images of the environment with object vehicle 1410 andpedestrian 1420 acquired at two different times.

FIG. 14B is a three-dimensional cube space illustrating two exemplaryfocus of expansions corresponding to the vehicle object and thepedestrian object shown in FIG. 14A. As shown in FIG. 14B, thedistribution of scale magnitude and focus of expansion of differentobjects in the static environment may be represented by a cylindricalshape 1430. The focus of expansion of object vehicle 1410 may berepresented by a circle 1440 centering around the cylindrical shape 1430at a certain scale magnitude, indicating that the focus of expansion ofobject vehicle 1410 is the same as the focus of expansion of the staticenvironment. FIG. 14B also shows a focus of expansion of pedestrian1420, as indicated by the circle 1450. The focus of expansion ofpedestrian 1420 may be located offset from the focus of expansion of thestatic environment and the focus of expansion of object vehicle 1410,which means at least one of the horizontal and the vertical coordinateof focus of expansion of pedestrian 1420 may be different from that ofthe focus of expansion of the static environment and/or object vehicle1410. The focus of expansion of pedestrian 1420 may be located on aplane corresponding to a first common scale magnitude, which may bedifferent from a second common scale magnitude corresponding to thefocus of expansion of object vehicle 1410.

In some embodiments, system 100 may detect an object crossing the roadin front of vehicle 200, and may cause vehicle 200 to brake to avoidcollision. Such an object may be referred to as a crossing object or acrossing-to-impact object, which may have a low time to collision withvehicle 200. The crossing object may likely collide with vehicle 200 ifthe crossing object and vehicle 200 continue to move in their currentlinear directions at their current substantially constant speed. FIG.15A shows a diagram illustrating an exemplary method of detecting acrossing-to-impact object. A crossing object 1500 may travel in adirection that crosses the travel direction of vehicle 200. If crossingobject 1500 and vehicle continue to travel at their respectivesubstantially constant speeds, the viewing angle from the camerainstalled on vehicle 200, a, will remain constant as crossing object1500 and vehicle 200 travel to point 1510. They may likely collide in afuture time at point 1510. System 100 may detect such a cross-to-impactobject and may take actions to avoid collision by, for example, brakingvehicle 200 before vehicle 200 and crossing object 1500 reach thecollision point 1510. Although FIG. 15A shows that crossing object 1500and vehicle 200 travel in directions that are perpendicular to eachother, their directions do not have to be perpendicular. For example,the angle between the direction of travel of crossing object 1500 andvehicle 200 may be any suitable value, such as 60 degrees, 45 degrees,etc.

In some embodiments, a constant viewing angle may be detected based onthe optical flow information. For example, when object 1500 moves with aconstant viewing angle, the object may appear fixed horizontally in animage plane and expands in a direction perpendicular to the image plane.When object 1500 moves with a constant viewing angle, the object 1500may have a focus of expansion, which may stay around a center in thehorizontal dimension of object 1500. Processing unit 110 may use suchinformation to determine whether object 1500 may collide with camerainstalled on vehicle 200.

FIG. 15B shows an image having a pedestrian 1520 (an exemplary crossingobject) crossing the road in front of vehicle 200. FIG. 15C shows animage having the pedestrian 1520 crossing the road in front of vehicle200 acquired at a time after the image shown in FIG. 15B is acquired(thus, pedestrian 1520 appears larger in FIG. 15C than in FIG. 15B). Avertical dotted line 1530 in FIG. 15B and FIG. 15C indicates thehorizontal coordinate of the focus of expansion of pedestrian 1520. Ifprocessing unit 110 detects that the vertical line 1530 stays around acenter in the horizontal dimension of pedestrian 1520 and does not moveaway from the center in the horizontal direction during the period oftime from the first image (e.g., FIG. 15B) to the second image (e.g.,FIG. 15C), processing unit 110 may determine that a viewing angle fromthe camera (e.g., one in the image capture device 122, 124, or 126)installed in vehicle 200 is constant. A constant viewing angle mayindicate that there is a high likelihood that vehicle 200 and pedestrian1520 will collide if they continue to move linearly at substantiallyconstant speeds in their respective directions that cross each other.When processing unit 110 determines that the horizontal coordinate ofthe focus of expansion of pedestrian 1520, as indicated by the verticalline 1530, stays around the center in the horizontal dimension ofpedestrian 1520, processing unit 110 may cause vehicle 200 to brake toavoid the collision as vehicle 200 approaches pedestrian 1520. Ifprocessing unit 110 detects that the line 1530 does not stay around thecenter in the horizontal dimension of pedestrian 1520 (e.g., moves awayfrom the center), which may indicate that the viewing angle is notconstant, processing unit 110 may not cause vehicle 200 to brake becausethe likelihood to have a collision may not be high.

FIG. 16 is a flowchart showing an exemplary process for detecting anobject in front of vehicle 200. Method 1600 may include acquiring aplurality of images of an area in front of vehicle 200 (step 1610). Forexample, at least one of image capture devices 122-126 may capture theplurality of images of the area in front of vehicle 200 at apredetermined interval, such as, for example, 0.01 second, 0.02 second,etc. Method 1600 may also include receiving the plurality of images(step 1620). For example, processing unit 110 may receive the pluralityof images from at least one of image capture devices 122-126. Method1600 may also include comparing a first image to a second image todetermine a focus of expansion of a crossing object moving in adirection that crosses a travel direction of the vehicle (step 1630).For example, processing unit 110 may compare the first and second imagesto obtain optical flow information, which includes displacement vectorsrelating to each first pixel in the first image and a correspondingsecond pixel in the second image. Processing unit 110 may determine thefocus of expansion based on the optical flow information.

Method 1600 may also include determine whether a horizontal coordinateof the focus of expansion stays around a center in the horizontaldimension of the crossing object during a period of time (step 1640).Processing unit 110 may compare the horizontal coordinate of the focusof expansion of the crossing object with the horizontal center (i.e.,horizontal coordinate) of the crossing object in the image, anddetermine whether they are the same or substantially the same (e.g.,within a small error, such as one or two pixels). When processing unit110 determines that the horizontal coordinate stays around the center inthe horizontal dimension of the crossing object during the period oftime (Yes, step 1640), processing unit 110 may cause vehicle 200 tobrake using, e.g., braking system 230, to avoid collision with thecrossing object (step 1650). When processing unit 110 determines thatthe horizontal coordinate does not stay within the crossing objectduring the period of time (No, step 1640), processing unit 110 mayexecutes steps 1610-1630.

The situation shown in FIG. 15A assumes that vehicle 200 and crossingobject 1500 may be represented by points. In reality, when camera isinstalled on vehicle 200, which may not be treated as a point, themethod discussed above for detecting a crossing-to-impact object may begeneralized to take into account the sizes of vehicle 200 and thecrossing object 1500. The generalized method may provide a visualindication of potential collision with a target object. For example, animage space of a target object may be extrapolated using any suitableextrapolation method to predict a future location of that target objectin an image space, in a future time, relative to vehicle 200, therebyshowing a boundary for avoiding collision with the target object.

As shown in FIG. 17, as vehicle 200 drives along the road where objectvehicles 1710, 1720, 1730 are parked, images of vehicles 1710, 1720,1730 may be extrapolated in time to predict the future position ofvehicles 1710, 1720, and 1730 relative to vehicle 200 in the imagespace. For example, an image of a portion of vehicle 1710 within a box1750 may be extrapolated in time to produce a predicted relativeposition of vehicle 1710, as indicated by box 1760. Similarextrapolation may be performed based on images of vehicles 1720 and1730. Lines 1770 and 1780 may be superimposed on the real-time imagecaptured by one of the image capture devices 122-126 to show thepredicted boundary for avoiding potential collision with parked vehicles1710-1730. To further assist the driver to prevent collision, in someembodiments, one or more lines 1740 representing a position of the frontbumper of vehicle 200 may be superimposed in the live, real-time image.Thus, when lines 1740 representing the front bumper do not touch theboundary lines 1770 and/or 1780, it provides an indication to the driverthat vehicle 200 is at a safe distance from vehicles 1710-1730 to avoidcollision. If lines 1740 touch the boundary lines 1770 and/or 1780, itmay indicate that vehicle 200 has a potential to collide with at leastone of parked vehicle 1710, 1720, or 1730, and system 100 may provide avisual and/or audio warning to the driver.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to the preciseforms or embodiments disclosed. Modifications and adaptations will beapparent to those skilled in the art from consideration of thespecification and practice of the disclosed embodiments. Additionally,although aspects of the disclosed embodiments are described as beingstored in memory, one skilled in the art will appreciate that theseaspects can also be stored on other types of computer readable media,such as secondary storage devices, for example, hard disks or CD ROM, orother forms of RAM or ROM, USB media, DVD, Blu-ray, or other opticaldrive media.

Computer programs based on the written description and disclosed methodsare within the skill of an experienced developer. The various programsor program modules can be created using any of the techniques known toone skilled in the art or can be designed in connection with existingsoftware. For example, program sections or program modules can bedesigned in or by means of .Net Framework, .Net Compact Framework (andrelated languages, such as Visual Basic, C, etc.), Java, C++,Objective-C, HTML, HTML/AJAX combinations, XML, or HTML with includedJava applets.

Moreover, while illustrative embodiments have been described herein, thescope of any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose skilled in the art based on the present disclosure. Thelimitations in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application.The examples are to be construed as non-exclusive. Furthermore, thesteps of the disclosed methods may be modified in any manner, includingby reordering steps and/or inserting or deleting steps. It is intended,therefore, that the specification and examples be considered asillustrative only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

What is claimed is:
 1. An object detecting system for a vehicle,comprising: at least one image capture device configured to acquire aplurality of images of an area in front of the vehicle; a datainterface; and at least one processing device programmed to: receive theplurality of images via the data interface; compare a first image to asecond image to determine displacement vectors between pixels of thefirst image and pixels of the second image; search for a region ofcoherent expansion that is a set of pixels in at least one of the firstimage and the second image, for which there exists a common focus ofexpansion and a common scale magnitude such that the set of pixelssatisfy a relationship between pixel positions, displacement vectors,the common focus of expansion, and the common scale magnitude; andidentify presence of a substantially upright object in at least one ofthe first image and the second image based on the set of pixels.
 2. Theobject detecting system of claim 1, wherein the region of coherentexpansion represents a substantially upright object that has a low timeto collision with the vehicle.
 3. The object detecting system of claim1, wherein the common focus of expansion represents a converging pointfor a plurality of displacement vectors associated with the set ofpixels.
 4. The object detecting system of claim 3, wherein the scalemagnitude associated with a pixel in the first image is defined as amagnitude of a displacement vector associated with the pixel divided bya distance from the pixel to the focus of expansion.
 5. The objectdetecting system of claim 4, wherein the at least one processing deviceis further programmed to: calculate a plurality of combinations of scalemagnitudes and focus of expansions for a plurality of pixels from atleast one of the first image and the second image, the plurality ofpixels including the set of pixels; and determine the common focus ofexpansion and the common scale: magnitude based on a distribution of theplurality of combinations of scale magnitudes and focus of expansions.6. The object detecting system of claim 5, wherein determining thecommon focus of expansion and the common scale magnitude based on thedistribution of the plurality of focuses of expansion includes:determining the common focus of expansion and the common scale magnitudeby determining that a density of distribution around the pair of commonfocus of expansion and the common scale magnitude is higher than apredetermined threshold.
 7. A vehicle, comprising: a body; at least oneimage capture device configured to acquire a plurality of images of anarea in front of the vehicle; a data interface; and at least oneprocessing device programmed to: receive the plurality of images via thedata interface; compare a first image to a second image to determinedisplacement vectors between pixels of the first image and pixels of thesecond image; search for a region of coherent expansion that is a set ofpixels in at least one of the first image and the second image, forwhich there exists a common focus of expansion and a common scalemagnitude such that the set of pixels satisfy a relationship betweenpixel positions, displacement vectors, the common focus of expansion,and the common scale magnitude; and identity presence of a substantiallyupright object in at least one of the first image and the second imagebased on the set of pixels.
 8. A method for detecting an object in frontof a vehicle, comprising: acquiring, via at least one image capturedevice, a plurality of images of an area in front of the vehicle;receiving, via a processing device, the plurality of images; comparing,via the processing device, a first image to a second image to determinedisplacement vectors between pixels of the first image and pixels of thesecond image; searching for, via the processing device, a region ofcoherent expansion that is a set of pixels in at least one of the firstimage and the second image, for which there exists a common focus ofexpansion and a common scale magnitude such that the set of pixelssatisfy a relationship between pixel positions, displacement vectors,the common focus of expansion, and the common scale magnitude; andidentifying, via the processing device, presence of a substantiallyupright object in at least one of the first image and the second imagebased on the set of pixels.
 9. The method of claim 8, furthercomprising: calculating a plurality of combinations of scale magnitudesand focus of expansions for a plurality of pixels from at least one ofthe first image and the second image, the plurality of pixels includingthe set of pixels; and determining the common focus of expansion and thecommon scale magnitude based on a distribution of the plurality ofcombinations of scale magnitudes and focus of expansions.
 10. The methodof claim 8, wherein determining the common focus of expansion and thecommon scale magnitude based on the distribution of the plurality offocuses of expansion includes: determining the common focus of expansionand the common scale magnitude by determining that a density ofdistribution around the pair of common focus of expansion and the commonscale magnitude is higher than a predetermined threshold.
 11. The objectdetecting system of claim 1, wherein the at least one processing deviceis further programmed to: determine whether a horizontal coordinate ofthe common focus of expansion stays around a center in a horizontaldimension during a period of time; and cause the vehicle to brake basedon the determination that the horizontal coordinate of the focus ofexpansion stays around the center in the horizontal dimension during theperiod of time.
 12. The object detecting system of claim 11, wherein theat least one processing device is further programmed to: determinewhether the substantially upright object will impact the camera based onthe determination that the horizontal coordinate of the focus ofexpansion stays around the center in the horizontal dimension.
 13. Theobject detecting system of claim 1, wherein determining the common focusof expansion comprises: providing a three-dimensional array comprising aplurality of cells, individual ones of the plurality of cells comprisingcoordinates of a focus of expansion; placing possible solutions to thecoordinates into the individual ones of the plurality of cells; andcounting the number of solutions within the individual ones of theplurality of cells.
 14. The vehicle of claim 7, wherein the region ofcoherent expansion represents that the substantially upright object hasa low time to collision with the vehicle.
 15. The vehicle of claim 7,wherein the common focus of expansion represents a converging point fora plurality of displacement vectors associated with the set of pixels.16. The vehicle of claim 7, wherein a scale magnitude associated with apixel in the first image is defined as a magnitude of a displacementvector associated with the pixel divided by a distance from the pixel tothe focus of expansion.
 17. The vehicle of claim 7, wherein the at leastone processing device is further programmed to: calculate a plurality ofcombinations of scale magnitudes and focus of expansions for a pluralityof pixels from at least one of the first image and the second image, theplurality of pixels including the set of pixels; and determine thecommon focus of expansion and the common scale: magnitude based on adistribution of the plurality of combinations of scale magnitudes andfocus of expansions.
 18. The vehicle of claim 17, wherein determiningthe common focus of expansion and the common scale magnitude based onthe distribution of the plurality of focuses of expansion includes:determining the common focus of expansion and the common scale magnitudeby determining that a density of distribution around the pair of commonfocus of expansion and the common scale magnitude is higher than apredetermined threshold.
 19. The method of claim 8, wherein the regionof coherent expansion represents that the substantially upright objecthas a low time to collision with the vehicle.
 20. The method of claim 8,wherein the common focus of expansion represents a converging point fora plurality of displacement vectors associated with the set of pixels.21. The method of claim 8, wherein a scale magnitude associated with apixel in the first image is defined as a magnitude of a displacementvector associated with the pixel divided by a distance from the pixel tothe focus of expansion.